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\begin{algorithm}[t]
\caption{Adv-NTK (Solving Eq.~(\ref{eq:adv-ntk-objective}) with SGD and GradNorm)}
\label{algo:advntk}
\begin{algorithmic}[1]
\Require
Training set $\mathcal{D}$, validation set size $M_{\mathrm{val}}$, learning rate $\zeta$, training iteration $T$, PGD function for finding adversarial validation d... | \begin{algorithm}
[t]
\caption{Adv-NTK (Solving Eq.~(\ref{eq:adv-ntk-objective}) with SGD and GradNorm)}
\begin{algorithmic}
[1]
\Require
Training set $\mathcal{D}$, validation set size $M_{\mathrm{val}}$, learning rate $\zeta$, training iteration $T$, PGD function for finding adversarial validation data.
\Ensure An in... | "https://arxiv.org/src/2310.06112" | "2310.06112.tar.gz" | "2024-02-04" | {
"title": "theoretical analysis of robust overfitting for wide dnns: an ntk approach",
"id": "2310.06112",
"abstract": "adversarial training (at) is a canonical method for enhancing the robustness of deep neural networks (dnns). however, recent studies empirically demonstrated that it suffers from robust... | "2024-03-15T07:33:05.157801" | {
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} | [] | "algorithm" | "241abda0-76ad-414a-9b6c-1ad836bfba2d" | 1161 | medium | |
\begin{algorithmic}
\Require $X_{0}$ initial condition,
$t$ simulation horizon.
\State Sample a realisation for $B_t$\,.
\If{$B_t = 1$}
\State Compute $\phi_V(u,t)$.
\State Run Algorithm \ref{alg:simul1}, using
$\phi_V(u,t)$ as CF.
\Else
\State $X_{t} = X_{0} e^{-bt}$.
\EndIf
\end{algorithmic}
| \begin{algorithmic}
\Require $X_{0}$ initial condition,
$t$ simulation horizon.
\State Sample a realisation for $B_t$\,.
\If{$B_t = 1$}
\State Compute $\phi_V(u,t)$.
\State Run Algorithm \ref{alg:simul1}, using
$\phi_V(u,t)$ as CF.
\Else
\State $X_{t} = X_{0} e^{-bt}$.
\EndIf
\end{algorithmic} | "https://arxiv.org/src/2401.15483" | "2401.15483.tar.gz" | "2024-01-27" | {
"title": "fast and general simulation of l\\'evy-driven ou processes for energy derivatives",
"id": "2401.15483",
"abstract": "l\\'evy-driven ornstein-uhlenbeck (ou) processes represent an intriguing class of stochastic processes that have garnered interest in the energy sector for their ability to capt... | "2024-03-15T05:30:23.512614" | {
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} | [] | "algorithm" | "8454da4e-1452-481d-9993-ad04f913b6c5" | 294 | easy | |
\begin{algorithm}
\floatname{algorithm}{\bf Algorithm}
\caption{Qualitative Test}
\vspace{4pt}
\hrule
\vspace{4pt}
\label{alg:qualitative}
\begin{algorithmic}[1]
\State Perform Step 1 - 5 in \textbf{Algorithm} \ref{alg:EFT} to obtain the critical value of EFT, $\hat{q}_{n,1-\alpha}$.
\State Formulate a projection prob... | \begin{algorithm}
\floatname{algorithm}{\bf Algorithm}
\caption{Qualitative Test}
\vspace{4pt}
\hrule
\vspace{4pt}
\begin{algorithmic}
[1]
\State Perform Step 1 - 5 in \textbf{Algorithm} \ref{alg:EFT} to obtain the critical value of EFT, $\hat{q}_{n,1-\alpha}$.
\State Formulate a projection problem as in \eqref{eq:opt}... | "https://arxiv.org/src/2310.11724" | "2310.11724.tar.gz" | "2024-02-26" | {
"title": "simultaneous nonparametric inference of m-regression under complex temporal dynamics",
"id": "2310.11724",
"abstract": "the paper considers simultaneous nonparametric inference for a wide class of m-regression models with time-varying coefficients. the covariates and errors of the regression m... | "2024-03-15T03:19:25.303660" | {
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} | [] | "algorithm" | "bf5fa5de-4a71-49da-a795-d4780ff193ae" | 710 | medium | |
\begin{algorithmic} [1]
\State Initialize actor network $\mu_\theta$ and critic networks $Q_{\omega_1}$ and $Q_{\omega_2}$.
\State Initialize corresponding target networks: $\theta' \leftarrow \theta$, ${\omega'}_1 \leftarrow \omega_1$, ${\omega'}_2 \leftarrow \omega_2$ and choose $\rho_\text{p} \in (0, 1)$.
\State Ini... | \begin{algorithmic}
[1]
\State Initialize actor network $\mu_\theta$ and critic networks $Q_{\omega_1}$ and $Q_{\omega_2}$.
\State Initialize corresponding target networks: $\theta' \leftarrow \theta$, ${\omega'}_1 \leftarrow \omega_1$, ${\omega'}_2 \leftarrow \omega_2$ and choose $\rho_\text{p} \in (0, 1)$.
\State Ini... | "https://arxiv.org/src/2211.02474" | "2211.02474.tar.gz" | "2024-02-15" | {
"title": "connecting stochastic optimal control and reinforcement learning",
"id": "2211.02474",
"abstract": "in this paper the connection between stochastic optimal control and reinforcement learning is investigated. our main motivation is to apply importance sampling to sampling rare events which can be... | "2024-03-15T04:03:02.445897" | {
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} | [] | "algorithm" | "64c02284-0165-4239-b815-a1defb3478dc" | 2714 | hard | |
\begin{algorithmic}[1]
\Require{$\{\beta_i\}_{i=1}^N, \{\alpha_i\}_{i=1}^N, \text{csm}, \mathbf{\hat{y}}, \lambda_1, \lambda_2, r, N, M, \mathbf{M_u}$.} \Comment{$\text{csm} = \{\text{csm}_1, \cdots, \text{csm}_n\}$, $M_u$ is the undersampling mask}
\State{$\mathbf{x}_{N} \sim \mathcal{N}(\mathbf{F}^{-1}(\mat... | \begin{algorithmic}
[1]
\Require{$\{\beta_i\}_{i=1}^N, \{\alpha_i\}_{i=1}^N, \text{csm}, \mathbf{\hat{y}}, \lambda_1, \lambda_2, r, N, M, \mathbf{M_u}$.} \Comment{$\text{csm} = \{\text{csm}_1, \cdots, \text{csm}_n\}$, $M_u$ is the undersampling mask}
\State{$\mathbf{x}_{N} \sim \mathcal{N}(\mathbf{F}^{-1}(\mathbf{M}_l\... | "https://arxiv.org/src/2208.05481" | "2208.05481.tar.gz" | "2024-01-20" | {
"title": "high-frequency space diffusion models for accelerated mri",
"id": "2208.05481",
"abstract": "diffusion models with continuous stochastic differential equations (sdes) have shown superior performances in image generation. it can serve as a deep generative prior to solving the inverse problem in m... | "2024-03-15T09:17:42.639884" | {
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} | [] | "algorithm" | "f40fb3f7-924b-4656-9ff3-dbe66c209e55" | 1965 | hard | |
\begin{algorithm}
State Smoothing
\end{algorithm}
| \begin{algorithm}
State Smoothing
\end{algorithm} | "https://arxiv.org/src/2402.08051" | "2402.08051.tar.gz" | "2024-02-12" | {
"title": "on bayesian filtering for markov regime switching models",
"id": "2402.08051",
"abstract": "this paper presents a framework for empirical analysis of dynamic macroeconomic models using bayesian filtering, with a specific focus on the state-space formulation of dynamic stochastic general equilibr... | "2024-03-15T04:21:14.605813" | {
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} | [] | "algorithm" | "4769e97e-aa4f-4112-8549-c210514be07f" | 49 | easy | |
\begin{algorithm}
\label{subalg}
\begin{description}
\item[{\sc Input:}]
Three integers $m$, $n$, and $r$ satisfying $0<2r \le n\le m$;
an $m\times n$ matrix $M$, given explicitly or implicitly.
\item[{\sc Initialization:}]
\begin{enumerate}
\item Generate a pair of independent abridged SRHT matrices $F$ a... | \begin{algorithm}
\begin{description}
\item[{\sc Input:}]
Three integers $m$, $n$, and $r$ satisfying $0<2r \le n\le m$;
an $m\times n$ matrix $M$, given explicitly or implicitly.
\item[{\sc Initialization:}]
\begin{enumerate}
\item Generate a pair of independent abridged SRHT matrices $F$ and $H$ of length 3 and sizes... | "https://arxiv.org/src/1906.04223" | "1906.04223.tar.gz" | "2024-01-06" | {
"title": "superfast escalators for near-optimal low rank approximation of a matrix",
"id": "1906.04223",
"abstract": "a superfast (aka sublinear cost) algorithm only accesses a small fraction of all entries of an input matrix. we seek such algorithms for low rank approximation (lra) of a matrix, but for s... | "2024-03-15T06:33:14.119039" | {
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} | [] | "algorithm" | "0c384e20-7482-42f6-a13b-fd99b46b8db8" | 974 | medium | |
\begin{algorithm}[h!]
\caption{Regression-Tree}\label{alg:rt}
\begin{algorithmic}
\State {\textbf{Input:}} $\{X_i,T_i,Y_i\}_{i=1}^n$, bandwidth $h$, tree depth $K$, number of features $L$
\If{K=1}
\State Return $(\arg\max_p\sum_{i=1}^nK(\frac{T_i-p}{h})\frac{Y_i}{f(T_i\mid X_i)}, \max_p\sum_{i=1}^nK(\frac{T_i-p}{h}... | \begin{algorithm}
[h!]
\caption{Regression-Tree}\begin{algorithmic}
\State {\textbf{Input:}} $\{X_i,T_i,Y_i\}_{i=1}^n$, bandwidth $h$, tree depth $K$, number of features $L$
\If{K=1}
\State Return $(\arg\max_p\sum_{i=1}^nK(\frac{T_i-p}{h})\frac{Y_i}{f(T_i\mid X_i)}, \max_p\sum_{i=1}^nK(\frac{T_i-p}{h})\frac{Y_i}{f(T_i\... | "https://arxiv.org/src/2402.02535" | "2402.02535.tar.gz" | "2024-02-04" | {
"title": "data-driven policy learning for a continuous treatment",
"id": "2402.02535",
"abstract": "this paper studies policy learning under the condition of unconfoundedness with a continuous treatment variable. our research begins by employing kernel-based inverse propensity-weighted (ipw) methods to es... | "2024-03-15T05:00:53.749181" | {
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} | [] | "algorithm" | "221f65db-97b3-4ab7-879f-0d36d700380f" | 985 | medium | |
\begin{algorithm}
\caption{VS-BO}
\begin{algorithmic}[1]
\State \textbf{Input}: $f(\mathbf{x})$, $\mathcal{X}=[0,1]^{D}$, $N_{init}$, $N$, $N_{vs}$
\State \textbf{Output}: Approximate maximizer $\mathbf{x}^{max}$
\State Initialize the set of $\mathbf{x}_{ipt}$ to be all variables in $\mathbf{x}$, $\ma... | \begin{algorithm}
\caption{VS-BO}
\begin{algorithmic}
[1]
\State \textbf{Input}: $f(\mathbf{x})$, $\mathcal{X}=[0,1]^{D}$, $N_{init}$, $N$, $N_{vs}$
\State \textbf{Output}: Approximate maximizer $\mathbf{x}^{max}$
\State Initialize the set of $\mathbf{x}_{ipt}$ to be all variables in $\mathbf{x}$, $\mathbf{x}_{ipt}=\ma... | "https://arxiv.org/src/2109.09264" | "2109.09264.tar.gz" | "2024-02-12" | {
"title": "computationally efficient high-dimensional bayesian optimization via variable selection",
"id": "2109.09264",
"abstract": "bayesian optimization (bo) is a method for globally optimizing black-box functions. while bo has been successfully applied to many scenarios, developing effective bo algor... | "2024-03-15T06:47:05.818231" | {
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} | [] | "algorithm" | "262efc37-f989-4cd7-afaf-cbb3afc5eae1" | 1563 | hard | |
\begin{algorithm}[H]
\begin{enumerate}
\item Fix a degree $d$, starting from $d=1$.
\item Find all curves at degree $d$, and form the set $\mathcal{S}:=\{\mathcal{C}\in \mathcal{M}_X|\text{deg}(\mathcal{C})=d\}$. Since at linear order $\psi^{\vec{n}}=q^{\vec{n}}$, the GW or GV invariants can be read... | \begin{algorithm}
[H]
\begin{enumerate}
\item Fix a degree $d$, starting from $d=1$.
\item Find all curves at degree $d$, and form the set $\mathcal{S}:=\{\mathcal{C}\in \mathcal{M}_X|\text{deg}(\mathcal{C})=d\}$. Since at linear order $\psi^{\vec{n}}=q^{\vec{n}}$, the GW or GV invariants can be read off from the coeff... | "https://arxiv.org/src/2303.00757" | "2303.00757.tar.gz" | "2024-01-19" | {
"title": "computational mirror symmetry",
"id": "2303.00757",
"abstract": "we present an efficient algorithm for computing the prepotential in compactifications of type ii string theory on mirror pairs of calabi-yau threefolds in toric varieties. applying this method, we exhibit the first systematic compu... | "2024-03-15T07:37:52.714955" | {
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} | [] | "algorithm" | "3bc12d6f-8beb-4572-95d6-9b50d57beb1f" | 1177 | hard | |
\label{alg:expansionrange}\begin{algorithmic}[1]
\State Input:
\begin{enumerate}
\item[1] Difference equation system $\Sigma$ with algebraic or analytic transcendental functions.
\item[2] For each parameter $\mu_i$ appearing as an argument of a transcendental function, user-specified acceptable domains for ea... | \begin{algorithmic}
[1]
\State Input:
\begin{enumerate}
\item[1] Difference equation system $\Sigma$ with algebraic or analytic transcendental functions.
\item[2] For each parameter $\mu_i$ appearing as an argument of a transcendental function, user-specified acceptable domains for each $\mu_i$, denoted $R_{\mu_i}$.
\i... | "https://arxiv.org/src/2401.16220" | "2401.16220.tar.gz" | "2024-01-29" | {
"title": "symbolic-numeric algorithm for parameter estimation in discrete-time models with $\\exp$",
"id": "2401.16220",
"abstract": "determining unknown parameter values in dynamic models is crucial for accurate analysis of the dynamics across the different scientific disciplines. discrete-time dynamic... | "2024-03-15T06:38:23.919697" | {
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} | [] | "algorithm" | "dd97095e-8c9d-408a-b12f-02545fcf6309" | 984 | medium | |
\begin{algorithm}[htpb]
\caption{Randomized $r$-sets-Douglas-Rachford with momentum (mRrDR) \label{r-mRDRK}}
\begin{algorithmic}
\Require
$A\in \mathbb{R}^{m\times n}$, $b\in \mathbb{R}^m$, $r\in\mathbb{Z}_{+}$, $k=1$, extrapolation/relaxation parameter $\alpha$, the heavy ball momentum parameter $\beta$, and in... | \begin{algorithm}
[htpb]
\caption{Randomized $r$-sets-Douglas-Rachford with momentum (mRrDR) }
\begin{algorithmic}
\Require
$A\in \mathbb{R}^{m\times n}$, $b\in \mathbb{R}^m$, $r\in\mathbb{Z}_{+}$, $k=1$, extrapolation/relaxation parameter $\alpha$, the heavy ball momentum parameter $\beta$, and initial vectors $x^1,x^... | "https://arxiv.org/src/2207.04291" | "2207.04291.tar.gz" | "2024-01-09" | {
"title": "randomized douglas-rachford methods for linear systems: improved accuracy and efficiency",
"id": "2207.04291",
"abstract": "the douglas-rachford (dr) method is a widely used method for finding a point in the intersection of two closed convex sets (feasibility problem). however, the method conv... | "2024-03-15T06:41:30.719861" | {
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} | [] | "algorithm" | "73720df1-3f10-487a-81bc-3caecb06dd30" | 1066 | medium | |
\begin{algorithm}
\caption{Rejection Sampling algorithm to draw $y^{PS = S4}|x$}\label{alg:one}
\begin{algorithmic}
\State \textbf{Input:} $x, \alpha, z, y$
\For{$i = 1 \text{ to } 8000$}
\State Draw $u_i$ from Uniform(0,1)
\State Draw $y^i_N$ from Normal(1.5, 4)
\State $C \gets \frac{f(y^i_N|PS = S4,x,a,z)... | \begin{algorithm}
\caption{Rejection Sampling algorithm to draw $y^{PS = S4}|x$}\begin{algorithmic}
\State \textbf{Input:} $x, \alpha, z, y$
\For{$i = 1 \text{ to } 8000$}
\State Draw $u_i$ from Uniform(0,1)
\State Draw $y^i_N$ from Normal(1.5, 4)
\State $C \gets \frac{f(y^i_N|PS = S4,x,a,z)}{M*f_{Y_N}(y^i_N)}$
\If{$u_... | "https://arxiv.org/src/2207.08964" | "2207.08964.tar.gz" | "2024-02-20" | {
"title": "sensitivity analysis for constructing optimal regimes in the presence of treatment non-compliance",
"id": "2207.08964",
"abstract": "the current body of research on developing optimal treatment strategies often places emphasis on intention-to-treat analyses, which fail to take into account the... | "2024-03-15T04:28:35.761087" | {
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} | [] | "algorithm" | "27b9cf55-cedc-40b2-bbe1-d571efbc07f6" | 500 | easy | |
\begin{algorithmic}
\Require {$\mathfrak{c} \in [4, 14]$, P} \Comment{P is the pattern set}
\State$i \gets 0,$
\State$sum\_{d_2} = 0$
\While{$i \leq |P|,$}
\State$\mathbf{x} \gets \mathfrak{b}^{(i)}$
\State$sum\_{d_1} = 0$
\While{$j\leq \mathfrak{K}$}
\State\Comment{$\mathfrak{K}$ : calculating the avg.}
\St... | \begin{algorithmic}
\Require {$\mathfrak{c} \in [4, 14]$, P} \Comment{P is the pattern set}
\State$i \gets 0,$
\State$sum\_{d_2} = 0$
\While{$i \leq |P|,$}
\State$\mathbf{x} \gets \mathfrak{b}^{(i)}$
\State$sum\_{d_1} = 0$
\While{$j\leq \mathfrak{K}$}
\State\Comment{$\mathfrak{K}$ : calculating the avg.}
\State$\mathfr... | "https://arxiv.org/src/2401.10922" | "2401.10922.tar.gz" | "2024-01-15" | {
"title": "a chaotic associative memory",
"id": "2401.10922",
"abstract": "we propose a novel chaotic associative memory model using a network of chaotic rossler systems and investigate the storage capacity and retrieval capabilities of this model as a function of increasing periodicity and chaos. in early... | "2024-03-15T07:01:32.792233" | {
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} | [] | "algorithm" | "534721e6-f901-46e1-b8d7-927fb0df9d3c" | 776 | medium | |
\begin{algorithm}[H]
\caption{$\alpha$ upper bound}%标题
\label{alpha-upperbound}%标签
\begin{algorithmic}[1]
\State $\boldsymbol{J}$ = $\emptyset$
\For{$i$ in range(0,$n-1$)}
\State $S_1(\alpha) = F_i^{-1}(1-\alpha/2)$,
\State $S_2(\alpha) = F_{i+1}^{-1}(\alpha/2)$,
\Stat... | \begin{algorithm}
[H]
\caption{$\alpha$ upper bound}%标题
%标签
\begin{algorithmic}
[1]
\State $\boldsymbol{J}$ = $\emptyset$
\For{$i$ in range(0,$n-1$)}
\State $S_1(\alpha) = F_i^{-1}(1-\alpha/2)$,
\State $S_2(\alpha) = F_{i+1}^{-1}(\alpha/2)$,
\State Let $S_1(\alpha) = S_2(\alpha)$, solve for solution $\alpha_i'$.
\If{$\... | "https://arxiv.org/src/2401.12237" | "2401.12237.tar.gz" | "2024-01-19" | {
"title": "a distribution-guided mapper algorithm",
"id": "2401.12237",
"abstract": "motivation: the mapper algorithm is an essential tool to explore shape of data in topology data analysis. with a dataset as an input, the mapper algorithm outputs a graph representing the topological features of the whole ... | "2024-03-15T07:04:57.607207" | {
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} | [] | "algorithm" | "f4ae2a51-cd74-47ef-8cf2-e956a4a80198" | 580 | easy | |
\begin{algorithm}
\caption{\texttt{Offline$\_$iCID(D,w,$\Psi$)}}
\label{shCHalgorithm}
\begin{algorithmic}[1]
\Statex \textbf{Input:} Dataset $D$;
Window Size $w$; Subsample Size List $\Psi$
\Statex \textbf{Output:} $C_{\psi^*}$ - a set of $N$ Interval Scores
\State Split $D$ into $N$ non-overlapping time intervals, ... | \begin{algorithm}
\caption{\texttt{Offline$\_$iCID(D,w,$\Psi$)}}
\begin{algorithmic}
[1]
\Statex \textbf{Input:} Dataset $D$;
Window Size $w$; Subsample Size List $\Psi$
\Statex \textbf{Output:} $C_{\psi^*}$ - a set of $N$ Interval Scores
\State Split $D$ into $N$ non-overlapping time intervals, each having $w$ points,... | "https://arxiv.org/src/2212.14630" | "2212.14630.tar.gz" | "2024-01-18" | {
"title": "detecting change intervals with isolation distributional kernel",
"id": "2212.14630",
"abstract": "detecting abrupt changes in data distribution is one of the most significant tasks in streaming data analysis. although many unsupervised change-point detection (cpd) methods have been proposed rec... | "2024-03-15T08:43:19.846400" | {
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} | [] | "algorithm" | "92cb38f5-2745-468d-9a05-031354d35734" | 615 | easy | |
\begin{algorithm}[!ht]
\caption{Integer Linear Programming for 3d Subroutine of 4d Sieve}
\begin{algorithmic}
\Require
\State \hspace{5mm}Boundary $[-B,B[\times[-B,B[\times[-B,B[\times[-B,B[$,
\State Plane $P$ defined by $u=(u_1,u_2,u_3,u_4),v=(v_1,v_2,v_3,v_4),R=(x,y,z,t)$
\State \hspace{5mm} such that $R \in ... | \begin{algorithm}
[!ht]
\caption{Integer Linear Programming for 3d Subroutine of 4d Sieve}
\begin{algorithmic}
\Require
\State \hspace{5mm}Boundary $[-B,B[\times[-B,B[\times[-B,B[\times[-B,B[$,
\State Plane $P$ defined by $u=(u_1,u_2,u_3,u_4),v=(v_1,v_2,v_3,v_4),R=(x,y,z,t)$
\State \hspace{5mm} such that $R \in P$, but... | "https://arxiv.org/src/2212.04999" | "2212.04999.tar.gz" | "2024-02-06" | {
"title": "an implementation of the extended tower number field sieve using 4d sieving in a box and a record computation in fp4",
"id": "2212.04999",
"abstract": "we report on an implementation of the extended tower number field sieve (extnfs) and record computation in a medium characteristic finite fiel... | "2024-03-15T07:40:23.873860" | {
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} | [] | "algorithm" | "746699e6-dbc0-4491-b2b5-8e540f97fd92" | 1486 | hard | |
\begin{algorithm}[t]
\caption{Successive elimination procedure for a static bandit with source data}
\label{alg:EA-TL-tabular}
\begin{algorithmic}[1]
\State{\textbf{Input:} state $s$, set of arms $\mathcal{I}$, source data $\mathcal{D}^{P}$.}
\State{Set $n_{k}^{P}(s)$ and $\overline{Y}_{k}^{P}(s)$ as in (\ref{eq:n-k-B... | \begin{algorithm}
[t]
\caption{Successive elimination procedure for a static bandit with source data}
\begin{algorithmic}
[1]
\State{\textbf{Input:} state $s$, set of arms $\mathcal{I}$, source data $\mathcal{D}^{P}$.}
\State{Set $n_{k}^{P}(s)$ and $\overline{Y}_{k}^{P}(s)$ as in (\ref{eq:n-k-B-Pdata-tabular}) and (\re... | "https://arxiv.org/src/2211.12612" | "2211.12612.tar.gz" | "2024-01-24" | {
"title": "transfer learning for contextual multi-armed bandits",
"id": "2211.12612",
"abstract": "motivated by a range of applications, we study in this paper the problem of transfer learning for nonparametric contextual multi-armed bandits under the covariate shift model, where we have data collected on ... | "2024-03-15T08:57:26.780784" | {
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} | [] | "algorithm" | "0406aca3-ff97-47f7-be18-cff47d084689" | 1704 | hard | |
\begin{algorithmic}
\State \textbf{Part 1 (Initialization)}:
$\mathcal{L}_0:=
\{\mathcal{F}_y^z \cup \mathcal{M}^{\tau}_{zU}\}$;
\ $\mathcal{B} = \{\eqref{VI5}\};$ \ $\mathcal{L'}_0:= \mathcal{L}_0 \cup \mathcal{B}$;
\ $\mathcal{U}_0:= \{\eqref{VI3};\eqref{VI4} \}$;
\ $\mathcal{A}_0:= C \setminus ... | \begin{algorithmic}
\State \textbf{Part 1 (Initialization)}:
$\mathcal{L}_0:=
\{\mathcal{F}_y^z \cup \mathcal{M}^{\tau}_{zU}\}$;
\ $\mathcal{B} = \{\eqref{VI5}\};$ \ $\mathcal{L'}_0:= \mathcal{L}_0 \cup \mathcal{B}$;
\ $\mathcal{U}_0:= \{\eqref{VI3};\eqref{VI4} \}$;
\ $\mathcal{A}_0:= C \setminus \mathcal{L}_0$; $\math... | "https://arxiv.org/src/2206.14340" | "2206.14340.tar.gz" | "2024-01-25" | {
"title": "drone-delivery network for opioid overdose -- nonlinear integer queueing-optimization models and methods",
"id": "2206.14340",
"abstract": "we propose a new stochastic emergency network design model that uses a fleet of drones to quickly deliver naxolone in response to opioid overdoses. the ne... | "2024-03-15T05:17:26.927464" | {
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"pr... | {
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} | [] | "algorithm" | "88852102-934c-4f4d-b173-49a9de868b6b" | 2436 | hard | |
\begin{algorithmic}[1]
\Require data matrix $X \in \mathbb{R}^{d \times n}_+$, number of features $k_{\text{max}}$, $\Gamma$, $\beta_{\text{max}}$, $c_j$ [for the $\ell_0$-forced version]
\State \textbf{Initialization} $\beta_{init}$%\leftarrow\frac{1}{2\lambda_{\text{max}}C_{x}}$
, $k\leftarrow1$, $w_1\leftarrow\sum_... | \begin{algorithmic}
[1]
\Require data matrix $X \in \mathbb{R}^{d \times n}_+$, number of features $k_{\text{max}}$, $\Gamma$, $\beta_{\text{max}}$, $c_j$ [for the $\ell_0$-forced version]
\State \textbf{Initialization} $\beta_{init}$%\leftarrow\frac{1}{2\lambda_{\text{max}}C_{x}}$
, $k\leftarrow1$, $w_1\leftarrow\sum_... | "https://arxiv.org/src/2210.02672" | "2210.02672.tar.gz" | "2024-01-18" | {
"title": "a novel maximum-entropy-driven technique for low-rank orthogonal nonnegative matrix factorization with $\\ell_0$-norm sparsity constraint",
"id": "2210.02672",
"abstract": "in data-driven control and machine learning, a common requirement involves breaking down large matrices into smaller, low... | "2024-03-15T05:57:43.061097" | {
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} | [] | "algorithm" | "2abf6474-22b6-4d90-a71b-5bb411e82410" | 1254 | hard | |
\begin{algorithmic}[1]
\State Initialize the teacher model $f_T(\cdot)$
\State $s \gets 0$ \Comment{Training steps for OD}
\While{$s < s_T$}
\State Sample a batch $\mathcal{B}$ from $\{(x_i, y_i)\}$
\State Train $f_T(\cdot)$ with cross-entropy loss on $\mathcal{B}$
\EndWhile
\State $s \gets 0$ \Comment{Training steps f... | \begin{algorithmic}
[1]
\State Initialize the teacher model $f_T(\cdot)$
\State $s \gets 0$ \Comment{Training steps for OD}
\While{$s < s_T$}
\State Sample a batch $\mathcal{B}$ from $\{(x_i, y_i)\}$
\State Train $f_T(\cdot)$ with cross-entropy loss on $\mathcal{B}$
\EndWhile
\State $s \gets 0$ \Comment{Training steps ... | "https://arxiv.org/src/2212.10558" | "2212.10558.tar.gz" | "2024-01-31" | {
"title": "on-the-fly denoising for data augmentation in natural language understanding",
"id": "2212.10558",
"abstract": "data augmentation (da) is frequently used to provide additional training data without extra human annotation automatically. however, data augmentation may introduce noisy data that i... | "2024-03-15T08:24:23.744468" | {
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} | [] | "algorithm" | "21d6797e-732e-458a-9764-e96bd0d834c9" | 699 | easy | |
\begin{algorithm}[!h]
\caption{Bounding sequence $c_{m, \delta}$} \label{alg_1}
\begin{algorithmic}[1]
\Statex {\bf Input:} $N$ sets of $p$-values generated by the joint null distribution of $\{z_1, \ldots z_m\}$
\Statex {\bf Output:} bounding sequences $c_{m, 0.5}$ and $c_{m, 1}$
\State {\bf for} $a=1, 2\ldo... | \begin{algorithm}
[!h]
\caption{Bounding sequence $c_{m, \delta}$} \begin{algorithmic}
[1]
\Statex {\bf Input:} $N$ sets of $p$-values generated by the joint null distribution of $\{z_1, \ldots z_m\}$
\Statex {\bf Output:} bounding sequences $c_{m, 0.5}$ and $c_{m, 1}$
\State {\bf for} $a=1, 2\ldots, N$ {\bf do}
\Stat... | "https://arxiv.org/src/2212.13574" | "2212.13574.tar.gz" | "2024-02-02" | {
"title": "weak signal inclusion under dependence and applications in genome-wide association study",
"id": "2212.13574",
"abstract": "motivated by the inquiries of weak signals in underpowered genome-wide association studies (gwass), we consider the problem of retaining true signals that are not strong ... | "2024-03-15T07:22:29.819002" | {
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} | [] | "algorithm" | "4388b530-2258-4782-adf6-3ab730dc68c6" | 914 | medium | |
\begin{algorithm}[H]
\caption{Dynamic Taylor}
\label{alg:dynamictaylor}
\begin{algorithmic}
\State Input:\begin{enumerate}
\item[1] Data input for Algorithm~\ref{alg:expansionrange}, e.g. system $\Sigma$.
\item[2] Output of Algorithm~\ref{alg:expansionrange}.
\item[3] Set number of terms, i.e., the highest ... | \begin{algorithm}
[H]
\caption{Dynamic Taylor}
\begin{algorithmic}
\State Input:\begin{enumerate}
\item[1] Data input for Algorithm~\ref{alg:expansionrange}, e.g. system $\Sigma$.
\item[2] Output of Algorithm~\ref{alg:expansionrange}.
\item[3] Set number of terms, i.e., the highest degree of Taylor polynomial, $N$.
\it... | "https://arxiv.org/src/2401.16220" | "2401.16220.tar.gz" | "2024-01-29" | {
"title": "symbolic-numeric algorithm for parameter estimation in discrete-time models with $\\exp$",
"id": "2401.16220",
"abstract": "determining unknown parameter values in dynamic models is crucial for accurate analysis of the dynamics across the different scientific disciplines. discrete-time dynamic... | "2024-03-15T06:24:53.153570" | {
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} | [] | "algorithm" | "50a133f5-3124-403f-b949-3e678220271d" | 1625 | hard | |
\begin{algorithm}
\caption{Nesterov's Accelerated Gradient Method as specified in \cite[\S 4.2]{li2023convex} }
\label{alg:NAG}
\begin{algorithmic}[1]
\Require $\theta_0 \in \mathbb{R}^n, m \in (0, \infty)$
\State $z_0 = \theta_0, B_0 = 0, A_0 = 1/m$
\For{$t = 0,...$}
\... | \begin{algorithm}
\caption{Nesterov's Accelerated Gradient Method as specified in \cite[\S 4.2]{li2023convex} }
\begin{algorithmic}
[1]
\Require $\theta_0 \in \mathbb{R}^n, m \in (0, \infty)$
\State $z_0 = \theta_0, B_0 = 0, A_0 = 1/m$
\For{$t = 0,...$}
\State $B_{t+1} = B_t + .5(1+\sqrt{4B_t+1})$
\State $A_{t+1} = B_{... | "https://arxiv.org/src/2309.10894" | "2309.10894.tar.gz" | "2024-02-15" | {
"title": "a novel gradient methodology with economical objective function evaluations for data science applications",
"id": "2309.10894",
"abstract": "gradient methods are experiencing a growth in methodological and theoretical developments owing to the challenges of optimization problems arising in dat... | "2024-03-15T05:06:27.333458" | {
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} | [] | "algorithm" | "2a277df0-9d4b-446c-a77e-edcb68b845e5" | 541 | easy | |
\begin{algorithm}
\caption{Variable Selection (line 8 in Algorithm~\ref{alg:VSBO})}
\begin{algorithmic}[1]
\State \textbf{Input}: $\mathcal{D}=\{(\mathbf{x}^{i},y^{i})\}_{i=1}^{t}$
\State \textbf{Output}: Set of important variables $\mathbf{x}_{ipt}$
\State Fit a GP to $\mathcal{D}$ and calculate impo... | \begin{algorithm}
\caption{Variable Selection (line 8 in Algorithm~\ref{alg:VSBO})}
\begin{algorithmic}
[1]
\State \textbf{Input}: $\mathcal{D}=\{(\mathbf{x}^{i},y^{i})\}_{i=1}^{t}$
\State \textbf{Output}: Set of important variables $\mathbf{x}_{ipt}$
\State Fit a GP to $\mathcal{D}$ and calculate important scores of v... | "https://arxiv.org/src/2109.09264" | "2109.09264.tar.gz" | "2024-02-12" | {
"title": "computationally efficient high-dimensional bayesian optimization via variable selection",
"id": "2109.09264",
"abstract": "bayesian optimization (bo) is a method for globally optimizing black-box functions. while bo has been successfully applied to many scenarios, developing effective bo algor... | "2024-03-15T06:19:42.461951" | {
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} | [] | "algorithm" | "17a45837-f966-4a93-8b42-c4f77ed5743b" | 1118 | medium | |
\begin{algorithm}[h]
\caption{Delta Particle Filter}
\label{alg:dpf}
\begin{enumerate}
\item{Input: data $y_{1:T}$, level $l\in\mathbb{N}$, particle number $N\in\mathbb{N}$ and parameter $\theta\in\Theta$.}
\item{Initialize: For $i\in\{1,\dots,N\}$, independently generate $\overline{W}_{\Delta_l:1}^{i,l}$ from $\mathca... | \begin{algorithm}
[h]
\caption{Delta Particle Filter}
\begin{enumerate}
\item{Input: data $y_{1:T}$, level $l\in\mathbb{N}$, particle number $N\in\mathbb{N}$ and parameter $\theta\in\Theta$.}
\item{Initialize: For $i\in\{1,\dots,N\}$, independently generate $\overline{W}_{\Delta_l:1}^{i,l}$ from $\mathcal{N}(0,\Delta_l... | "https://arxiv.org/src/2310.03114" | "2310.03114.tar.gz" | "2024-02-19" | {
"title": "bayesian parameter inference for partially observed stochastic volterra equations",
"id": "2310.03114",
"abstract": "in this article we consider bayesian parameter inference for a type of partially observed stochastic volterra equation (sve). sves are found in many areas such as physics and ma... | "2024-03-15T05:09:03.161347" | {
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} | [] | "algorithm" | "17852f5e-7351-4d19-9943-a69ce6cc989f" | 2563 | hard | |
\begin{algorithm}
\caption{\texttt{IBB} (Independent Block Bootstrap)}
\label{alg:ibb}
\begin{algorithmic}[1]
\Require $(X_1, \ldots, X_n)$, $(Y_1, \ldots, Y_n)$, $d$
\State $N \gets \lfloor n/d \rfloor$
\For{$k = 1, \ldots, N$}
\State $B_{X,k} \gets (X_{(k-1)d + 1}, \ldots, X_{kd})$
\State $B... | \begin{algorithm}
\caption{\texttt{IBB} (Independent Block Bootstrap)}
\begin{algorithmic}
[1]
\Require $(X_1, \ldots, X_n)$, $(Y_1, \ldots, Y_n)$, $d$
\State $N \gets \lfloor n/d \rfloor$
\For{$k = 1, \ldots, N$}
\State $B_{X,k} \gets (X_{(k-1)d + 1}, \ldots, X_{kd})$
\State $B_{Y,k} \gets (Y_{(k-1)d + 1}, \ldots, Y_{... | "https://arxiv.org/src/2112.14091" | "2112.14091.tar.gz" | "2024-02-05" | {
"title": "a bootstrap test for independence of time series based on the distance covariance",
"id": "2112.14091",
"abstract": "we present a test for independence of two strictly stationary time series based on a bootstrap procedure for the distance covariance. our test detects any kind of dependence bet... | "2024-03-15T07:11:00.545120" | {
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} | [] | "algorithm" | "fb8ae28c-85f5-4740-a170-b955b44525d6" | 873 | medium | |
\begin{algorithmic}[1]
\State $x_{\emptyset} \leftarrow 0$
\For{$k = 1, \ldots, n$}
\For{each subset $N \subseteq [n]$ with $|N| = k$}
\State $x_N \leftarrow \infty$
\For{each agent $i \in N$}
\State $y \leftarrow \textsc{Mark}_i(x_{N \setminus \{i\}}, 1/n)$
\State \a... | \begin{algorithmic}
[1]
\State $x_{\emptyset} \leftarrow 0$
\For{$k = 1, \ldots, n$}
\For{each subset $N \subseteq [n]$ with $|N| = k$}
\State $x_N \leftarrow \infty$
\For{each agent $i \in N$}
\State $y \leftarrow \textsc{Mark}_i(x_{N \setminus \{i\}}, 1/n)$
\State \algorithmicif \ $y < x_N$ \algorithmicthen \ $x_N \l... | "https://arxiv.org/src/2312.15326" | "2312.15326.tar.gz" | "2024-02-13" | {
"title": "on connected strongly-proportional cake-cutting",
"id": "2312.15326",
"abstract": "we investigate the problem of fairly dividing a divisible heterogeneous resource, also known as a cake, among a set of agents. we characterize the existence of an allocation in which every agent receives a contigu... | "2024-03-15T04:11:23.042663" | {
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} | [] | "algorithm" | "ea33ced4-5e0f-460e-9fab-72ff75af8966" | 527 | easy | |
\begin{algorithm}[!ht]\caption{Dynamic KDE, query part, informal version of Algorithm~\ref{alg:dynamic_KDE_query}}\label{alg:dynamic_KDE_query_pseudo}
\begin{algorithmic}[1]
\State {\bf data structure} \textsc{DynamicKDE} \Comment{Theorem~\ref{thm:main_result}}
\State
\Procedure{\textsc{Query}}{$q\in \mathbb{R}^d, \e... | \begin{algorithm}[!ht]
\caption{Dynamic KDE, query part, informal version of Algorithm~\ref{alg:dynamic_KDE_query}}\begin{algorithmic}
[1]
\State {\bf data structure} \textsc{DynamicKDE} \Comment{Theorem~\ref{thm:main_result}}
\State
\Procedure{\textsc{Query}}{$q\in \mathbb{R}^d, \epsilon \in (0,1),f_{\mathsf{KDE}} \in... | "https://arxiv.org/src/2208.03915" | "2208.03915.tar.gz" | "2024-02-13" | {
"title": "dynamic maintenance of kernel density estimation data structure: from practice to theory",
"id": "2208.03915",
"abstract": "kernel density estimation (kde) stands out as a challenging task in machine learning. the problem is defined in the following way: given a kernel function $f(x,y)$ and a ... | "2024-03-15T05:48:56.340651" | {
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} | [] | "algorithm" | "0efb6973-2e92-4be0-8f7b-42c96d7445a5" | 888 | medium | |
\begin{algorithmic}
\Require $s_0 = 4$, $\mu_0 = 0$, $\phi_0 = 0.95$, $\sigma^{2}_{\eta,0} = 0.02$
\For{\texttt{b in} $1:B_{draws}$}
\State \text{Sample states (Kalman Filter and Smoother): } $\boldsymbol{h}_b \sim h|y^{\ast}, s_{b-1}, \phi_{b-1}, \sigma^{2}_{\eta... | \begin{algorithmic}
\Require $s_0 = 4$, $\mu_0 = 0$, $\phi_0 = 0.95$, $\sigma^{2}_{\eta,0} = 0.02$
\For{\texttt{b in} $1:B_{draws}$}
\State \text{Sample states (Kalman Filter and Smoother): } $\boldsymbol{h}_b \sim h|y^{\ast}, s_{b-1}, \phi_{b-1}, \sigma^{2}_{\eta,b-1}, \mu_{b-1}$
\State \text{Sample mixture indicators... | "https://arxiv.org/src/2402.12384" | "2402.12384.tar.gz" | "2024-01-27" | {
"title": "comparing mcmc algorithms in stochastic volatility models using simulation based calibration",
"id": "2402.12384",
"abstract": "simulation based calibration (sbc) is applied to analyse two commonly used, competing markov chain monte carlo algorithms for estimating the posterior distribution of... | "2024-03-15T03:27:09.183136" | {
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} | [] | "algorithm" | "26e708d1-288f-488f-adf7-39c41848f54f" | 830 | medium | |
\begin{algorithmic}[1]
\State $\eta_1 = \eta$
\State $\theta_1^{in} = \theta_0$
\For{$b = 1,..., B$}
\State Run SGD with constant step size $\eta_b$ for $t_b$ steps, starting from $\theta_{b}^{in}$
\State Let the last update be $\theta_{b}^{last}$
\State $D_b = \textbf{Diagnostic}(\eta_b, w, l, q, \theta_{b}^{last})$
\... | \begin{algorithmic}
[1]
\State $\eta_1 = \eta$
\State $\theta_1^{in} = \theta_0$
\For{$b = 1,..., B$}
\State Run SGD with constant step size $\eta_b$ for $t_b$ steps, starting from $\theta_{b}^{in}$
\State Let the last update be $\theta_{b}^{last}$
\State $D_b = \textbf{Diagnostic}(\eta_b, w, l, q, \theta_{b}^{last})$
... | "https://arxiv.org/src/1910.08597" | "1910.08597.tar.gz" | "2024-02-16" | {
"title": "robust learning rate selection for stochastic optimization via splitting diagnostic",
"id": "1910.08597",
"abstract": "this paper proposes splitsgd, a new dynamic learning rate schedule for stochastic optimization. this method decreases the learning rate for better adaptation to the local geom... | "2024-03-15T04:35:48.772484" | {
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} | [] | "algorithm" | "2f38469c-5a96-40ca-9473-84809ef12b14" | 551 | easy | |
\begin{algorithm}{({\bf Numerical Computation of the Projector onto ${\cal A}$})} \label{alg:projA}\
\begin{description}
\item[Step 0] ({\em Initialization}) The following are given: Current iterate $u^-$, the system and control matrices $A(t)$ and $B(t)$, the numbers of state and control variables $n$ and $m$, and the... | \begin{algorithm}
{({\bf Numerical Computation of the Projector onto ${\cal A}$})} \
\begin{description}
\item[Step 0] ({\em Initialization}) The following are given: Current iterate $u^-$, the system and control matrices $A(t)$ and $B(t)$, the numbers of state and control variables $n$ and $m$, and the initial and ter... | "https://arxiv.org/src/2210.17279" | "2210.17279.tar.gz" | "2024-01-11" | {
"title": "douglas--rachford algorithm for control-constrained minimum-energy control problems",
"id": "2210.17279",
"abstract": "splitting and projection-type algorithms have been applied to many optimization problems due to their simplicity and efficiency, but the application of these algorithms to opt... | "2024-03-15T06:24:12.690702" | {
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} | [] | "algorithm" | "54fc2259-a324-4057-a8ea-94663b696837" | 1095 | medium | |
\begin{algorithmic}[1]
\Statex \textbf{Input:} $\phi$, $\theta$, initial episodes $K_{\mathrm{init}}$, total budget of episodes $K_{\mathrm{E}}$,
\Statex \textbf{Init:} $\phi' \gets \phi$, $\theta' \gets \theta$, $\mathcal{D} \gets \emptyset$
\For{each initial episode $1,\dots,K_{\mathrm{init}}$}
\State Sample a batch ... | \begin{algorithmic}
[1]
\Statex \textbf{Input:} $\phi$, $\theta$, initial episodes $K_{\mathrm{init}}$, total budget of episodes $K_{\mathrm{E}}$,
\Statex \textbf{Init:} $\phi' \gets \phi$, $\theta' \gets \theta$, $\mathcal{D} \gets \emptyset$
\For{each initial episode $1,\dots,K_{\mathrm{init}}$}
\State Sample a batch... | "https://arxiv.org/src/2303.17615" | "2303.17615.tar.gz" | "2024-01-30" | {
"title": "utilizing reinforcement learning for de novo drug design",
"id": "2303.17615",
"abstract": "deep learning-based approaches for generating novel drug molecules with specific properties have gained a lot of interest in the last few years. recent studies have demonstrated promising performance for ... | "2024-03-15T06:00:14.855698" | {
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} | [] | "algorithm" | "2909d43e-dd04-41e9-bbc4-dd4424606c0b" | 1810 | hard | |
\begin{algorithmic}[1]
\Require $\mathcal{G} = (V, E) $, $\mathbf{H}^{(t-1)}=\left[\mathbf{h}_1^{(t-1)},\cdots \mathbf{h}_n^{(t-1)}\right]$, $\mathbf{A}^{(0)}$;
\Ensure $\mathbf{A}^{(t)}$;
\renewcommand{\algorithmicensure}{\textbf{Hyper-paramters:}}
\Ensure: $m$, $K$, $\gamma$
\State Initialization: $... | \begin{algorithmic}[1]
\Require $\mathcal{G} = (V, E) $, $\mathbf{H}^{(t-1)}=\left[\mathbf{h}_1^{(t-1)},\cdots \mathbf{h}_n^{(t-1)}\right]$, $\mathbf{A}^{(0)}$;
\Ensure $\mathbf{A}^{(t)}$;
\renewcommand{\algorithmicensure}{\textbf{Hyper-paramters:}}
\Ensure: $m$, $K$, $\gamma$
\State Initialization: $J_s = \phi$
\For{$... | "https://arxiv.org/src/2103.07295" | "2103.07295.tar.gz" | "2024-01-24" | {
"title": "adversarial graph disentanglement",
"id": "2103.07295",
"abstract": "a real-world graph has a complex topological structure, which is often formed by the interaction of different latent factors. however, most existing methods lack consideration of the intrinsic differences in relations between n... | "2024-03-15T08:52:54.851311" | {
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"p... | {
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} | [] | "algorithm" | "3b89d2ce-94a1-4475-bcd1-65dcab9b6817" | 1545 | hard | |
\begin{algorithm}[!ht]
\caption{A weighted training approach for A/B tests}
\label{algo:weighted}
\begin{algorithmic}[1]
\Require{The probability of treatment assignment: $p$; a model class for the weight prediction: $\mathcal{G}=\{G_{\theta_W}: \mathbb{R}^d \rightarrow \{0,1\}, {\theta_W}\in \Theta_W\}$; the mach... | \begin{algorithm}
[!ht]
\caption{A weighted training approach for A/B tests}
\begin{algorithmic}
[1]
\Require{The probability of treatment assignment: $p$; a model class for the weight prediction: $\mathcal{G}=\{G_{\theta_W}: \mathbb{R}^d \rightarrow \{0,1\}, {\theta_W}\in \Theta_W\}$; the machine learning model class:... | "https://arxiv.org/src/2310.17496" | "2310.17496.tar.gz" | "2024-02-03" | {
"title": "tackling interference induced by data training loops in a/b tests: a weighted training approach",
"id": "2310.17496",
"abstract": "in modern recommendation systems, the standard pipeline involves training machine learning models on historical data to predict user behaviors and improve recommen... | "2024-03-15T04:57:23.023908" | {
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} | [] | "algorithm" | "c64b9dc5-8108-4824-8172-022fd9a4541c" | 1683 | hard | |
\begin{algorithmic}
\Require $a_i, B_i, \theta_i$
\\
\State Sort the values $a_i, \theta_i$ according to $\frac{a_{ij}}{\theta_{ij}}$ in a descending order.
If there are goods with $\theta_{ij} = 0$, sort them separately according to $a_{ij}$ and place them as a prefix (lower indices) before the other sorted goods. Equ... | \begin{algorithmic}
\Require $a_i, B_i, \theta_i$
\\
\State Sort the values $a_i, \theta_i$ according to $\frac{a_{ij}}{\theta_{ij}}$ in a descending order.
If there are goods with $\theta_{ij} = 0$, sort them separately according to $a_{ij}$ and place them as a prefix (lower indices) before the other sorted goods. Equ... | "https://arxiv.org/src/2307.04108" | "2307.04108.tar.gz" | "2024-01-15" | {
"title": "asynchronous proportional response dynamics in markets with adversarial scheduling",
"id": "2307.04108",
"abstract": "we study proportional response dynamics (prd) in linear fisher markets where participants act asynchronously. we model this scenario as a sequential process in which in every s... | "2024-03-15T06:09:48.864469" | {
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} | [] | "algorithm" | "d65b5b49-084b-4b17-931b-f38be4063e2c" | 664 | easy | |
\begin{algorithmic}[1]
\State $D_{train} \gets$ Initialize training set
\While {$C(fake)>threshold$} \Comment{The critic can be optimized until $C(fake)$ is near $0$. }
\State Randomly select a discrete variable $c$ with equal probability
\State Compute the probability mass ... | \begin{algorithmic}
[1]
\State $D_{train} \gets$ Initialize training set
\While {$C(fake)>threshold$} \Comment{The critic can be optimized until $C(fake)$ is near $0$. }
\State Randomly select a discrete variable $c$ with equal probability
\State Compute the probability mass function (PMF) of $c$
\State Randomly select... | "https://arxiv.org/src/2207.12255" | "2207.12255.tar.gz" | "2024-02-15" | {
"title": "implementing a hierarchical deep learning approach for simulating multi-level auction data",
"id": "2207.12255",
"abstract": "we present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. the complexities encountered in thi... | "2024-03-15T03:57:59.120702" | {
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} | [] | "algorithm" | "72d87570-5113-4a95-bec3-f8f6162007db" | 1446 | hard | |
\begin{algorithm}
\caption{Algorithm to solve the optimisation problem \ref{met:merge}}\label{alg:cap}
\begin{algorithmic}
\Require Dataset $\mathbf{Y}$ and hyperparameter $\beta$
\State $\mathbf{w}= \mathbf{1}$
\For{$i \in$ $\{1,...,I\}$}
\State Solve the regression problem equation \ref{met:reg2}
... | \begin{algorithm}
\caption{Algorithm to solve the optimisation problem \ref{met:merge}}\begin{algorithmic}
\Require Dataset $\mathbf{Y}$ and hyperparameter $\beta$
\State $\mathbf{w}= \mathbf{1}$
\For{$i \in$ $\{1,...,I\}$}
\State Solve the regression problem equation \ref{met:reg2}
\State $\mathbf{h} = \mathbf{Y} \mat... | "https://arxiv.org/src/2312.02867" | "2312.02867.tar.gz" | "2024-02-16" | {
"title": "semi-supervised health index monitoring with feature generation and fusion",
"id": "2312.02867",
"abstract": "the health index (hi) is crucial for evaluating system health, aiding tasks like anomaly detection and predicting remaining useful life for systems demanding high safety and reliabilit... | "2024-03-15T05:16:16.260093" | {
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} | [] | "algorithm" | "36aa95b3-dcde-4a29-af77-c90cd4c08e0d" | 606 | easy | |
\begin{algorithm}[ht]
\caption{Existence of a connected strongly-proportional allocation for $n$ hungry agents.}
\label{alg:hungry}
\begin{algorithmic}[1]
\For{$t = 1, \ldots, n-1$}
\State $z \leftarrow \textsc{Mark}_1(0, t/n)$ \algorithmiccomment{agent $1$'s $t/n$-mark}
\For{$i = 2, \ldots, n$}
... | \begin{algorithm}
[ht]
\caption{Existence of a connected strongly-proportional allocation for $n$ hungry agents.}
\begin{algorithmic}
[1]
\For{$t = 1, \ldots, n-1$}
\State $z \leftarrow \textsc{Mark}_1(0, t/n)$ \algorithmiccomment{agent $1$'s $t/n$-mark}
\For{$i = 2, \ldots, n$}
\State \algorithmicif \ $\textsc{Mark}_i... | "https://arxiv.org/src/2312.15326" | "2312.15326.tar.gz" | "2024-02-13" | {
"title": "on connected strongly-proportional cake-cutting",
"id": "2312.15326",
"abstract": "we investigate the problem of fairly dividing a divisible heterogeneous resource, also known as a cake, among a set of agents. we characterize the existence of an allocation in which every agent receives a contigu... | "2024-03-15T04:18:36.086397" | {
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} | [] | "algorithm" | "5aa6a9d2-695a-437b-95df-88078efc2922" | 510 | easy | |
\begin{algorithm}[t!]
\begin{algorithmic}
\caption{Pseudo Python Code for $\texttt{TracInAD}$}\label{alg:tracinad}
\Require{$
\mathcal{D}_{train}, \mathcal{D}_{val}, \{\theta_{t_1},\dots,\theta_{t_n}\},
\{\eta_{t_1},\dots,\eta_{t_n}\},$ \newline
\hspace*{3em} $\ell(\... | \begin{algorithm}
[t!]
\begin{algorithmic}
\caption{Pseudo Python Code for $\texttt{TracInAD}$} \Require{$
\mathcal{D}_{train}, \mathcal{D}_{val}, \{\theta_{t_1},\dots,\theta_{t_n}\},
\{\eta_{t_1},\dots,\eta_{t_n}\},$ \newline
\hspace*{3em} $\ell(\theta,.), m$}
\State $\texttt{TracInAD} \gets dict()$
\State $B \gets$ r... | "https://arxiv.org/src/2205.01362" | "2205.01362.tar.gz" | "2024-01-30" | {
"title": "tracinad: measuring influence for anomaly detection",
"id": "2205.01362",
"abstract": "as with many other tasks, neural networks prove very effective for anomaly detection purposes. however, very few deep-learning models are suited for detecting anomalies on tabular datasets. this paper proposes... | "2024-03-15T08:20:53.958646" | {
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} | [] | "algorithm" | "8a702937-20e8-40f0-9099-a6c05cd1b23a" | 677 | easy | |
\begin{algorithmic}[1]
\For{$k=0,1,2,\ldots$}
\Statex \textcolor{blue}{\textbf{Local updates}} for all clients: \State $v^k_{x_m} = \alpha x_m^k + (1 - \alpha) u_{x_m}^k$, \ \ \ \ $v^k_{y_m} = \alpha y_m^k + (1 - \alpha) u_{y_m}^k$ \label{alg1:line2}
\Statex
All clients \textcolor{red}{\textbf{communicate}} ... | \begin{algorithmic}
[1]
\For{$k=0,1,2,\ldots$}
\Statex \textcolor{blue}{\textbf{Local updates}} for all clients: \State $v^k_{x_m} = \alpha x_m^k + (1 - \alpha) u_{x_m}^k$, \ \ \ \ $v^k_{y_m} = \alpha y_m^k + (1 - \alpha) u_{y_m}^k$ \Statex
All clients \textcolor{red}{\textbf{communicate}} to locally compute
\State $... | "https://arxiv.org/src/2106.07289" | "2106.07289.tar.gz" | "2024-01-24" | {
"title": "decentralized personalized federated learning for min-max problems",
"id": "2106.07289",
"abstract": "personalized federated learning (pfl) has witnessed remarkable advancements, enabling the development of innovative machine learning applications that preserve the privacy of training data. howe... | "2024-03-15T08:58:57.459725" | {
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} | [] | "algorithm" | "f9c36080-24d7-4be2-811f-215eea97a00d" | 1440 | hard | |
\begin{algorithm}[h!]
\caption{Motifs mining}
\label{alg:1}
\textbf{Input:}
Training set samples $\textbf{T}$ with labeled binary classes C = [0, 1]\\
\textbf{Output:}
Extracted motifs for each class
\begin{algorithmic}[1]
\State Motifs = $\emptyset$
\State N = length($\textbf{T}$[0])
\Comment{Number of time series ... | \begin{algorithm}
[h!]
\caption{Motifs mining}
\textbf{Input:}
Training set samples $\textbf{T}$ with labeled binary classes C = [0, 1]\\
\textbf{Output:}
Extracted motifs for each class
\begin{algorithmic}
[1]
\State Motifs = $\emptyset$
\State N = length($\textbf{T}$[0])
\Comment{Number of time series samples}
\State... | "https://arxiv.org/src/2211.04411" | "2211.04411.tar.gz" | "2024-02-01" | {
"title": "motif-guided time series counterfactual explanations",
"id": "2211.04411",
"abstract": "with the rising need of interpretable machine learning methods, there is a necessity for a rise in human effort to provide diverse explanations of the influencing factors of the model decisions. to improve th... | "2024-03-15T08:04:45.521940" | {
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} | [] | "algorithm" | "df0aeb78-e184-4489-8ac7-f74592b0da58" | 1108 | medium | |
\begin{algorithmic}
\State Initialize $O\gets \emptyset$.\footnote{This can be replaced with any other menu of public goods with no change to the analysis below.}
\While{$O$ is not $(t,u)$-stable}
\If{$O$ is not $t$-feasible}
\State By definition there exists $j\in O$ such that $|j \succ O \setminus \{ j \} | <t$.
\St... | \begin{algorithmic}
\State Initialize $O\gets \emptyset$.\footnote{This can be replaced with any other menu of public goods with no change to the analysis below.}
\While{$O$ is not $(t,u)$-stable}
\If{$O$ is not $t$-feasible}
\State By definition there exists $j\in O$ such that $|j \succ O \setminus \{ j \} | <t$.
\Sta... | "https://arxiv.org/src/2402.11370" | "2402.11370.tar.gz" | "2024-02-17" | {
"title": "stable menus of public goods: a matching problem",
"id": "2402.11370",
"abstract": "we study a matching problem between agents and public goods, in settings without monetary transfers. since goods are public, they have no capacity constraints. there is no exogenously defined budget of goods to b... | "2024-03-15T03:42:28.124433" | {
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} | [] | "algorithm" | "889ab22e-62aa-4dae-b920-32e61e523162" | 1084 | medium | |
\begin{algorithmic}[1]
\State Solve \eqref{intrphi} for first-order correctors $\phi_i$.
\State Determine the homogenized coefficients $a_h$ via \eqref{intrhomcoeff}.
\State Solve \eqref{intruhtilde} for $\tilde{u}_h$ on $\partial Q_L$ by $\tilde{u}_h = \int G_h*(\nabla\cdot g)$.
\State Solve \eqref{eqn:intrsig} for fi... | \begin{algorithmic}
[1]
\State Solve \eqref{intrphi} for first-order correctors $\phi_i$.
\State Determine the homogenized coefficients $a_h$ via \eqref{intrhomcoeff}.
\State Solve \eqref{intruhtilde} for $\tilde{u}_h$ on $\partial Q_L$ by $\tilde{u}_h = \int G_h*(\nabla\cdot g)$.
\State Solve \eqref{eqn:intrsig} for f... | "https://arxiv.org/src/2109.01616" | "2109.01616.tar.gz" | "2024-01-11" | {
"title": "optimal artificial boundary conditions based on second-order correctors for three dimensional random elliptic media",
"id": "2109.01616",
"abstract": "we are interested in numerical algorithms for computing the electrical field generated by a charge distribution localized on scale $\\ell$ in a... | "2024-03-15T06:22:17.156672" | {
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} | [] | "algorithm" | "06cf420c-4958-4598-b8dc-76acfcd98366" | 584 | easy | |
\begin{algorithmic}
\State Draw $X_b, Y_b, [Z_{k-1,b} \cdots Z_{0,b}]$ \Comment{Draw training batch and corresponding features from prior models}
\State $Z_{k,b} \gets f^l_k(X_b)$
\State $N, D \gets shape(Z_{k,b})$
\State $\hat{Y_b} \gets f_k(X_b)$
\State $\mathcal{L} \gets \mathcal{L}_{ce}(\hat{Y_b}, Y_b)$
\State $i \... | \begin{algorithmic}
\State Draw $X_b, Y_b, [Z_{k-1,b} \cdots Z_{0,b}]$ \Comment{Draw training batch and corresponding features from prior models}
\State $Z_{k,b} \gets f^l_k(X_b)$
\State $N, D \gets shape(Z_{k,b})$
\State $\hat{Y_b} \gets f_k(X_b)$
\State $\mathcal{L} \gets \mathcal{L}_{ce}(\hat{Y_b}, Y_b)$
\State $i \... | "https://arxiv.org/src/2207.09031" | "2207.09031.tar.gz" | "2024-02-16" | {
"title": "decorrelative network architecture for robust electrocardiogram classification",
"id": "2207.09031",
"abstract": "artificial intelligence has made great progress in medical data analysis, but the lack of robustness and trustworthiness has kept these methods from being widely deployed. as it is... | "2024-03-15T04:53:44.109126" | {
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} | [] | "algorithm" | "b8eb4c1a-b47e-4a8c-8972-f01cba2327ce" | 820 | medium | |
\begin{algorithmic}[1]
\State Input:\begin{itemize}
\item Algebraic system of difference equations named $\Sigma'$
\item Time measured data allowing prolongation of the system.
\item For $\bar{\mu}=\mu_1,\ldots,\mu_n$ the finite set of parameters, the data $R_{\mu_i}$ of ... | \begin{algorithmic}
[1]
\State Input:\begin{itemize}
\item Algebraic system of difference equations named $\Sigma'$
\item Time measured data allowing prolongation of the system.
\item For $\bar{\mu}=\mu_1,\ldots,\mu_n$ the finite set of parameters, the data $R_{\mu_i}$ of permissible intervals for each parameter value.... | "https://arxiv.org/src/2401.16220" | "2401.16220.tar.gz" | "2024-01-29" | {
"title": "symbolic-numeric algorithm for parameter estimation in discrete-time models with $\\exp$",
"id": "2401.16220",
"abstract": "determining unknown parameter values in dynamic models is crucial for accurate analysis of the dynamics across the different scientific disciplines. discrete-time dynamic... | "2024-03-15T06:24:53.153570" | {
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} | [] | "algorithm" | "39bf72af-e57a-41d1-aab2-3034eaaea5b1" | 1500 | hard | |
\begin{algorithm}\caption{Greedy}\label{alg:bffg}
\begin{algorithmic}[1]
\State Initialize: $A_0\gets \Phi$
\For {$i \in [m]$}
\State Let $u_i$ be the element $u\in P_i$ maximizing $f(u~|~A_{i-1}) := f(A_{i-1}\cup \{u\}) - f(A_{i-1})$.
\State $A_i\gets A_{i-1}\cup \{u_i\}$
\EndFor
\end{algorithmic}
\end{algor... | \begin{algorithm}
\caption{Greedy}\begin{algorithmic}
[1]
\State Initialize: $A_0\gets \Phi$
\For {$i \in [m]$}
\State Let $u_i$ be the element $u\in P_i$ maximizing $f(u~|~A_{i-1}) := f(A_{i-1}\cup \{u\}) - f(A_{i-1})$.
\State $A_i\gets A_{i-1}\cup \{u_i\}$
\EndFor
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2208.03367" | "2208.03367.tar.gz" | "2024-02-12" | {
"title": "sublinear time algorithm for online weighted bipartite matching",
"id": "2208.03367",
"abstract": "online bipartite matching is a fundamental problem in online algorithms. the goal is to match two sets of vertices to maximize the sum of the edge weights, where for one set of vertices, each verte... | "2024-03-15T06:18:53.303533" | {
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} | [] | "algorithm" | "297c8742-3dd4-4758-8cc8-94d9e5e6b061" | 300 | easy | |
\begin{algorithmic}[1]
\State Set $\lambda^{(0)},\rho^{(0)}$. Choose $\epsilon_1^{(0)},\epsilon_2^{(0)}$.
\State Obtain initial HS optical flow $\textbf{u}^{(0)}$
\For{$n = 1,2,\dots$ until convergence \textbf{do}}
\State update $\textbf{u}^{(n)}, d^{(n)}$
\If $\|B\textbf{u}^{(n)}-c\|_{\mathcal{H}}\le \max\{... | \begin{algorithmic}
[1]
\State Set $\lambda^{(0)},\rho^{(0)}$. Choose $\epsilon_1^{(0)},\epsilon_2^{(0)}$.
\State Obtain initial HS optical flow $\textbf{u}^{(0)}$
\For{$n = 1,2,\dots$ until convergence \textbf{do}}
\State update $\textbf{u}^{(n)}, d^{(n)}$
\If $\|B\textbf{u}^{(n)}-c\|_{\mathcal{H}}\le \max\{\epsilon_1... | "https://arxiv.org/src/2011.12267" | "2011.12267.tar.gz" | "2024-02-21" | {
"title": "a framework for fluid motion estimation using a constraint-based refinement approach",
"id": "2011.12267",
"abstract": "physics-based optical flow models have been successful in capturing the deformities in fluid motion arising from digital imagery. however, a common theoretical framework anal... | "2024-03-15T04:18:37.012979" | {
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} | [] | "algorithm" | "3976d7de-c44d-4516-a2e2-893596fb61ab" | 807 | medium | |
\begin{algorithm}[ht]
\floatname{algorithm}{Problem}
\caption{The reinsurer's time-selection problem}
Given any initial $(t,y,z)$, the reinsurer chooses a time $p(t;c)\in[t, T]\bigcup\{\infty\}$ at which his risk exposure increases by $\bar{y}-y$ and he obtains a premium of $(\bar{y}-y)\kappa\big(p(t;c)\big)$. His obje... | \begin{algorithm}
[ht]
\floatname{algorithm}{Problem}
\caption{The reinsurer's time-selection problem}
Given any initial $(t,y,z)$, the reinsurer chooses a time $p(t;c)\in[t, T]\bigcup\{\infty\}$ at which his risk exposure increases by $\bar{y}-y$ and he obtains a premium of $(\bar{y}-y)\kappa\big(p(t;c)\big)$. His obj... | "https://arxiv.org/src/2402.11580" | "2402.11580.tar.gz" | "2024-02-18" | {
"title": "stackelberg reinsurance and premium decisions with mv criterion and irreversibility",
"id": "2402.11580",
"abstract": "we study a reinsurance stackelberg game in which both the insurer and the reinsurer adopt the mean-variance (abbr. mv) criterion in their decision-making and the reinsurance i... | "2024-03-15T03:31:21.490355" | {
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} | [] | "algorithm" | "488a833c-7569-4972-8f3a-04b397d23278" | 413 | easy | |
\begin{algorithm}
\caption{PITT numerical update scheme}
\label{alg:pitt_numerical_update}
\begin{algorithmic}
\Require $V_0$, $T_{h1}$, $T_{h2}$, time $t$, $L$ layers
\For{$l = 1,2,\ldots,L$}
\State $X_l \gets Dropout(LA(T_{h1}, T_{h2}, V_{l-1})$
\State $t_l \gets MLP\left(\frac{l\cdot t}{L... | \begin{algorithm}
\caption{PITT numerical update scheme}
\begin{algorithmic}
\Require $V_0$, $T_{h1}$, $T_{h2}$, time $t$, $L$ layers
\For{$l = 1,2,\ldots,L$}
\State $X_l \gets Dropout(LA(T_{h1}, T_{h2}, V_{l-1})$
\State $t_l \gets MLP\left(\frac{l\cdot t}{L}\right)$
\State $V_l \gets V_{l-1} + MLP(\left[X_{l}, t_{l}\r... | "https://arxiv.org/src/2305.08757" | "2305.08757.tar.gz" | "2024-02-12" | {
"title": "physics informed token transformer for solving partial differential equations",
"id": "2305.08757",
"abstract": "solving partial differential equations (pdes) is the core of many fields of science and engineering. while classical approaches are often prohibitively slow, machine learning models... | "2024-03-15T05:13:40.045735" | {
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} | [] | "algorithm" | "dda431c9-ce60-47d9-a4f8-038519606958" | 369 | easy | |
\begin{algorithm}[H]
\caption{Square system method}
\label{alg:squresystem}
\begin{algorithmic}[1]
\State Input:\begin{itemize}
\item Algebraic system of difference equations named $\Sigma'$
\item Time measured data allowing prolongation of the system.
\item For $\bar{\mu... | \begin{algorithm}
[H]
\caption{Square system method}
\begin{algorithmic}
[1]
\State Input:\begin{itemize}
\item Algebraic system of difference equations named $\Sigma'$
\item Time measured data allowing prolongation of the system.
\item For $\bar{\mu}=\mu_1,\ldots,\mu_n$ the finite set of parameters, the data $R_{\mu_i... | "https://arxiv.org/src/2401.16220" | "2401.16220.tar.gz" | "2024-01-29" | {
"title": "symbolic-numeric algorithm for parameter estimation in discrete-time models with $\\exp$",
"id": "2401.16220",
"abstract": "determining unknown parameter values in dynamic models is crucial for accurate analysis of the dynamics across the different scientific disciplines. discrete-time dynamic... | "2024-03-15T06:24:53.153570" | {
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... | {
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} | [] | "algorithm" | "31237cdf-ff3a-45c1-a76b-89b571059859" | 1569 | hard | |
\begin{algorithm}[]
\caption{Outer Approximation Branch-and-Cut Algorithm (OA-B\&C)} \label{algo_oa-bc}
{\small
\begin{algorithmic}
\State \textbf{Part 1 (Initialization)}:
$\mathcal{L}_0:=
\{\mathcal{F}_y^z \cup \mathcal{M}^{\tau}_{zU}\}$;
\ $\mathcal{B} = \{\eqref{VI5}\};$ \ $\mathcal{L'}_0:= \math... | \begin{algorithm}[]
\caption{Outer Approximation Branch-and-Cut Algorithm (OA-B\&C)} {\small
\begin{algorithmic}
\State \textbf{Part 1 (Initialization)}:
$\mathcal{L}_0:=
\{\mathcal{F}_y^z \cup \mathcal{M}^{\tau}_{zU}\}$;
\ $\mathcal{B} = \{\eqref{VI5}\};$ \ $\mathcal{L'}_0:= \mathcal{L}_0 \cup \mathcal{B}$;
\ $\mathc... | "https://arxiv.org/src/2206.14340" | "2206.14340.tar.gz" | "2024-01-25" | {
"title": "drone-delivery network for opioid overdose -- nonlinear integer queueing-optimization models and methods",
"id": "2206.14340",
"abstract": "we propose a new stochastic emergency network design model that uses a fleet of drones to quickly deliver naxolone in response to opioid overdoses. the ne... | "2024-03-15T05:17:26.927464" | {
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} | [] | "algorithm" | "8d03fd35-cfba-42a6-9a5a-003641134809" | 2548 | hard | |
\begin{algorithmic}[1]
\Statex \textbf{Inputs:} $s,i,d,a,r,e,v,h$, daily vaccinations
\Statex \textbf{Output:}
{$\vec{\beta}_{uu}, \vec{\beta}_{vu}, \vec{\beta}_{vv}$, $\vec{\beta}_{uv}$}
\Statex \textbf{Initialization:} $n=7$ or $n= 14$
\For{each time step... | \begin{algorithmic}
[1]
\Statex \textbf{Inputs:} $s,i,d,a,r,e,v,h$, daily vaccinations
\Statex \textbf{Output:}
{$\vec{\beta}_{uu}, \vec{\beta}_{vu}, \vec{\beta}_{vv}$, $\vec{\beta}_{uv}$}
\Statex \textbf{Initialization:} $n=7$ or $n= 14$
\For{each time step $j$}
\State Select window $z_{j}=\{j-n+1,...,j\}$
\State Init... | "https://arxiv.org/src/2401.06629" | "2401.06629.tar.gz" | "2024-01-12" | {
"title": "pandemic infection forecasting through compartmental model and learning-based approaches",
"id": "2401.06629",
"abstract": "the emergence and spread of deadly pandemics has repeatedly occurred throughout history, causing widespread infections and loss of life. the rapid spread of pandemics hav... | "2024-03-15T07:35:55.770135" | {
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} | [] | "algorithm" | "9ae7b949-ca00-44a3-8ca5-f5e9a4f79d28" | 1174 | hard | |
\begin{algorithmic}[1]
\State \textbf{Input:} matrix $M \in \mathbb{R}^{n\times m}$ with orthonormal columns
\State \textbf{Output:} index set $I$ if cardinality $m$
\State $I = \{\mathsf{argmax}\ |M(:,1)|\}$
\For $k = 2,\dots,m$
\State $c = M(I,1:k-1)^{-1} M(I,k)$
\State $r = M(:,k) - M(:,1:k-1)c$
\State $I = I \cup \... | \begin{algorithmic}
[1]
\State \textbf{Input:} matrix $M \in \mathbb{R}^{n\times m}$ with orthonormal columns
\State \textbf{Output:} index set $I$ if cardinality $m$
\State $I = \{\mathsf{argmax}\ |M(:,1)|\}$
\For $k = 2,\dots,m$
\State $c = M(I,1:k-1)^{-1} M(I,k)$
\State $r = M(:,k) - M(:,1:k-1)c$
\State $I = I \cup ... | "https://arxiv.org/src/2211.11338" | "2211.11338.tar.gz" | "2024-02-25" | {
"title": "approximation in the extended functional tensor train format",
"id": "2211.11338",
"abstract": "this work proposes the extended functional tensor train (eftt) format for compressing and working with multivariate functions on tensor product domains. our compression algorithm combines tensorized c... | "2024-03-15T03:21:44.181934" | {
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} | [] | "algorithm" | "6af1a0d0-3663-4af3-a5c2-abd7fb6c009f" | 371 | easy | |
\begin{algorithm}
\caption{General strategy for permutation testing by betting} \label{alg:general}
\hspace*{\algorithmicindent} \textbf{Input:} Sequence of test statistics $Y_0,Y_1, Y_2, \ldots$.\\
\textbf{Optional Input:} Stopping rule $\mathcal S$, potentially data-dependent and decided on the fly.\\
\hspace*{\al... | \begin{algorithm}
\caption{General strategy for permutation testing by betting} \hspace*{\algorithmicindent} \textbf{Input:} Sequence of test statistics $Y_0,Y_1, Y_2, \ldots$.\\
\textbf{Optional Input:} Stopping rule $\mathcal S$, potentially data-dependent and decided on the fly.\\
\hspace*{\algorithmicindent} \textb... | "https://arxiv.org/src/2401.07365" | "2401.07365.tar.gz" | "2024-02-18" | {
"title": "sequential monte-carlo testing by betting",
"id": "2401.07365",
"abstract": "in a monte-carlo test, the observed dataset is fixed, and several resampled or permuted versions of the dataset are generated in order to test a null hypothesis that the original dataset is exchangeable with the resampl... | "2024-03-15T05:18:01.125945" | {
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} | [] | "algorithm" | "472eefd0-75c7-43d8-8f88-ed86e45b1a37" | 910 | medium | |
\begin{algorithm}
\caption{Non-Accelerated Composite Stochastic Mirror-Descent (NACSMD)}
\label{Algo NACSMD}
\begin{algorithmic}
\Require Number of iterations $T \geq 0$, starting point $x_1 \in \mathcal{X}$, step-sizes $(\alpha_t,\gamma_t)_t$%, proximal function $V$
\For{$ 1 \leq t \leq T$}
\begin{equation}
x_{t+1} ... | \begin{algorithm}
\caption{Non-Accelerated Composite Stochastic Mirror-Descent (NACSMD)}
\begin{algorithmic}
\Require Number of iterations $T \geq 0$, starting point $x_1 \in \mathcal{X}$, step-sizes $(\alpha_t,\gamma_t)_t$%, proximal function $V$
\For{$ 1 \leq t \leq T$}
\begin{equation*}
x_{t+1} = \arg\min_{x\in {\ca... | "https://arxiv.org/src/2211.01758" | "2211.01758.tar.gz" | "2024-01-23" | {
"title": "optimal algorithms for stochastic complementary composite minimization",
"id": "2211.01758",
"abstract": "inspired by regularization techniques in statistics and machine learning, we study complementary composite minimization in the stochastic setting. this problem corresponds to the minimizatio... | "2024-03-15T05:49:42.262116" | {
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} | [] | "algorithm" | "b3434f1f-2c3d-4043-8650-b931b344b5c0" | 568 | easy | |
\begin{algorithmic}[1]
\State $\beta \gets \beta^*$ \Comment{load best set of parameters for BidNet}
\State $\alpha \gets \alpha^*$ \Comment{load optimized set of parameters for synthesizer}
\State $\tilde{\mathbf{c}}\sim A_{\alpha^*}(\mathbf{z})$\Comment{sample synthetic examples from the t... | \begin{algorithmic}
[1]
\State $\beta \gets \beta^*$ \Comment{load best set of parameters for BidNet}
\State $\alpha \gets \alpha^*$ \Comment{load optimized set of parameters for synthesizer}
\State $\tilde{\mathbf{c}}\sim A_{\alpha^*}(\mathbf{z})$\Comment{sample synthetic examples from the trained synthesizer}
\State ... | "https://arxiv.org/src/2207.12255" | "2207.12255.tar.gz" | "2024-02-15" | {
"title": "implementing a hierarchical deep learning approach for simulating multi-level auction data",
"id": "2207.12255",
"abstract": "we present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. the complexities encountered in thi... | "2024-03-15T04:05:27.239976" | {
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} | [] | "algorithm" | "7752de0d-02c9-458d-beae-0182637b3cb3" | 1064 | medium | |
\begin{algorithm}
\caption{Imprecise Bayesian Neural Network}\label{alg:ibnn}
\begin{algorithmic}
\item
\textbf{S1} Specify a \textit{finite} set $\mathcal{P}$ of plausible prior probabilities on the parameters $\theta$ of the neural network, and a \textit{finite} set $\mathcal{L}_{x,\theta}$ of plausible likelihoods.... | \begin{algorithm}
\caption{Imprecise Bayesian Neural Network}\begin{algorithmic}
\item
\textbf{S1} Specify a \textit{finite} set $\mathcal{P}$ of plausible prior probabilities on the parameters $\theta$ of the neural network, and a \textit{finite} set $\mathcal{L}_{x,\theta}$ of plausible likelihoods.
\item
\textbf{S2}... | "https://arxiv.org/src/2302.09656" | "2302.09656.tar.gz" | "2024-02-22" | {
"title": "credal bayesian deep learning",
"id": "2302.09656",
"abstract": "uncertainty quantification and robustness to distribution shifts are important goals in machine learning and artificial intelligence. although bayesian neural networks (bnns) allow for uncertainty in the predictions to be assessed,... | "2024-03-15T04:08:18.406723" | {
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} | [] | "algorithm" | "af210405-ee4d-43d4-affe-786399b7c612" | 484 | easy | |
\begin{algorithmic}
\Function{NewtonOptimization}{$\bold{M}$, $\boldsymbol{\beta}$, $\boldsymbol{\phi}$}\Comment{$\bold{M}$ represents moments; $\boldsymbol{\beta}$ is the initial value of parameters; $\boldsymbol{\phi}$ represents sufficient statistics. }
\State $converged \gets $False$;\ n \gets 0;\ tol \gets 1\times... | \begin{algorithmic}
\Function{NewtonOptimization}{$\bold{M}$, $\boldsymbol{\beta}$, $\boldsymbol{\phi}$}\Comment{$\bold{M}$ represents moments; $\boldsymbol{\beta}$ is the initial value of parameters; $\boldsymbol{\phi}$ represents sufficient statistics. }
\State $converged \gets $False$;\ n \gets 0;\ tol \gets 1\times... | "https://arxiv.org/src/2303.02898" | "2303.02898.tar.gz" | "2024-02-19" | {
"title": "stabilizing the maximal entropy moment method for rarefied gas dynamics at single-precision",
"id": "2303.02898",
"abstract": "the maximal entropy moment method (mem) is systematic solution of the challenging problem: generating extended hydrodynamic equations valid for both dense and rarefied... | "2024-03-15T03:56:31.323537" | {
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} | [] | "algorithm" | "d072fad7-9556-42f5-951f-77688060ae01" | 3209 | hard | |
\begin{algorithmic}[1]
\Require Baseline covariates $Z_1, \cdots, Z_n$
\State Estimate $\bar{\mu}_i$ by regressing baseline outcomes on covariates $Z$
\State Estimate $\bar{\sigma}^2$ the variance of the residuals from this regression
\State Consider the range of values $\bar{\psi} \in [\bar{\s... | \begin{algorithmic}
[1]
\Require Baseline covariates $Z_1, \cdots, Z_n$
\State Estimate $\bar{\mu}_i$ by regressing baseline outcomes on covariates $Z$
\State Estimate $\bar{\sigma}^2$ the variance of the residuals from this regression
\State Consider the range of values $\bar{\psi} \in [\bar{\sigma}^2, 4 \bar{\sigma}^... | "https://arxiv.org/src/2310.14983" | "2310.14983.tar.gz" | "2024-01-13" | {
"title": "causal clustering: design of cluster experiments under network interference",
"id": "2310.14983",
"abstract": "this paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers. we provide a framework to choose the clustering tha... | "2024-03-15T06:10:54.538396" | {
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} | [] | "algorithm" | "ce86669e-8b17-4c81-9cb9-f33153413764" | 507 | easy | |
\begin{algorithmic}[1]
\State $\mathcal{F} \leftarrow \textsc{makePF}(\{\{r\} | r \in \mathcal{R}\})$
\State converged $\leftarrow \texttt{false}$
\While{\texttt{not} converged}
\State converged $\leftarrow \texttt{true}$
\State $\mathcal{F}_0 \leftarrow \textsc{SSF}(\mathcal{F}, k)$
... | \begin{algorithmic}
[1]
\State $\mathcal{F} \leftarrow \textsc{makePF}(\{\{r\} | r \in \mathcal{R}\})$
\State converged $\leftarrow \texttt{false}$
\While{\texttt{not} converged}
\State converged $\leftarrow \texttt{true}$
\State $\mathcal{F}_0 \leftarrow \textsc{SSF}(\mathcal{F}, k)$
\State $\mathcal{F}' \leftarrow \e... | "https://arxiv.org/src/2311.00964" | "2311.00964.tar.gz" | "2024-01-17" | {
"title": "on finding bi-objective pareto-optimal fraud prevention rule sets for fintech applications",
"id": "2311.00964",
"abstract": "rules are widely used in fintech institutions to make fraud prevention decisions, since rules are highly interpretable thanks to their intuitive if-then structure. in p... | "2024-03-15T05:59:22.584765" | {
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} | [] | "algorithm" | "56433a36-7e84-4ae9-bed2-37f1f6867574" | 734 | medium | |
\begin{algorithmic}[1]
\Statex {\bf Input:} $p$-values of the observed test statistics and bounding sequences $c_{m, 0.5}$ and $c_{m, 1}$
\Statex {\bf Output:} a proportion estimate $\hat \pi$
\State Rank the variables by their $p$-values so that $p_{(1)} < p_{(2)} < \ldots < p_{(m)}$
\State Compute
\[
\... | \begin{algorithmic}
[1]
\Statex {\bf Input:} $p$-values of the observed test statistics and bounding sequences $c_{m, 0.5}$ and $c_{m, 1}$
\Statex {\bf Output:} a proportion estimate $\hat \pi$
\State Rank the variables by their $p$-values so that $p_{(1)} < p_{(2)} < \ldots < p_{(m)}$
\State Compute
\[
\hat{\pi}_{0.5}... | "https://arxiv.org/src/2212.13574" | "2212.13574.tar.gz" | "2024-02-02" | {
"title": "weak signal inclusion under dependence and applications in genome-wide association study",
"id": "2212.13574",
"abstract": "motivated by the inquiries of weak signals in underpowered genome-wide association studies (gwass), we consider the problem of retaining true signals that are not strong ... | "2024-03-15T07:22:29.819002" | {
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} | [] | "algorithm" | "fa3e3b7f-a246-4d6f-9354-0f8675d5fcd7" | 598 | easy | |
\begin{algorithmic}[1]
\State \textbf{Input}: iteration index $t$, $\mathcal{D}=\{(\mathbf{x}^{i},y^{i})\}_{i=1}^{t}$, $N_{init}$, $N_{vs}$, set of important variables chosen at iteration $t-N_{vs}$, denote as $\hat{\mathbf{x}}_{ipt}$
\State \textbf{Output}: Set of important variables chosen at iter... | \begin{algorithmic}
[1]
\State \textbf{Input}: iteration index $t$, $\mathcal{D}=\{(\mathbf{x}^{i},y^{i})\}_{i=1}^{t}$, $N_{init}$, $N_{vs}$, set of important variables chosen at iteration $t-N_{vs}$, denote as $\hat{\mathbf{x}}_{ipt}$
\State \textbf{Output}: Set of important variables chosen at iteration $t$, denote a... | "https://arxiv.org/src/2109.09264" | "2109.09264.tar.gz" | "2024-02-12" | {
"title": "computationally efficient high-dimensional bayesian optimization via variable selection",
"id": "2109.09264",
"abstract": "bayesian optimization (bo) is a method for globally optimizing black-box functions. while bo has been successfully applied to many scenarios, developing effective bo algor... | "2024-03-15T06:07:56.040616" | {
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} | [] | "algorithm" | "2762af07-844b-4fc9-aafb-1ea1c5c8f367" | 831 | medium | |
\begin{algorithmic}
\Require $n=2$, $N\in\mathbb{N}$ sufficiently large
\Require $X_{1i}$ are independent for $i=0,1,...,k-1$
\Require $X_{2i}=1+r$, $r>-1$ for $i=0,1,...,k-1$
\Require $\boldsymbol\pi_{i}=(q_i(\widehat{W}_i,A_i),1-q_i(\widehat{W}_i,A_i))$ for $i=0,1,...,k-1$
\State $l\gets 0$\Comment{initialize $l$}
\W... | \begin{algorithmic}
\Require $n=2$, $N\in\mathbb{N}$ sufficiently large
\Require $X_{1i}$ are independent for $i=0,1,...,k-1$
\Require $X_{2i}=1+r$, $r>-1$ for $i=0,1,...,k-1$
\Require $\boldsymbol\pi_{i}=(q_i(\widehat{W}_i,A_i),1-q_i(\widehat{W}_i,A_i))$ for $i=0,1,...,k-1$
\State $l\gets 0$\Comment{initialize $l$}
\W... | "https://arxiv.org/src/2402.17164" | "2402.17164.tar.gz" | "2024-02-26" | {
"title": "withdrawal success optimization in a pooled annuity fund",
"id": "2402.17164",
"abstract": "consider a closed pooled annuity fund investing in n assets with discrete-time rebalancing. at time 0, each annuitant makes an initial contribution to the fund, committing to a predetermined schedule of w... | "2024-03-15T02:40:56.763732" | {
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} | [] | "algorithm" | "da14d1df-06be-4198-a9fa-cf514273b72c" | 1170 | hard | |
\begin{algorithm}
\caption{Discrete Soft Actor-Critic for \textit{de novo} drug design}\label{alg:sac}
\begin{algorithmic}[1]
\Statex \textbf{Input:} $\phi$, $\theta$, initial episodes $K_{\mathrm{init}}$, total budget of episodes $K_{\mathrm{E}}$,
\Statex \textbf{Init:} $\phi' \gets \phi$, $\theta' \gets \theta$, $\ma... | \begin{algorithm}
\caption{Discrete Soft Actor-Critic for \textit{de novo} drug design}\begin{algorithmic}
[1]
\Statex \textbf{Input:} $\phi$, $\theta$, initial episodes $K_{\mathrm{init}}$, total budget of episodes $K_{\mathrm{E}}$,
\Statex \textbf{Init:} $\phi' \gets \phi$, $\theta' \gets \theta$, $\mathcal{D} \gets ... | "https://arxiv.org/src/2303.17615" | "2303.17615.tar.gz" | "2024-01-30" | {
"title": "utilizing reinforcement learning for de novo drug design",
"id": "2303.17615",
"abstract": "deep learning-based approaches for generating novel drug molecules with specific properties have gained a lot of interest in the last few years. recent studies have demonstrated promising performance for ... | "2024-03-15T06:00:14.855698" | {
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} | [] | "algorithm" | "5bc27b20-1fab-47d0-b048-85fe081cac5c" | 1913 | hard | |
\begin{algorithm}[h!]
\caption{Incremental learning for infection rates prediction}
\label{alg:nnmethod}
\begin{algorithmic}[1]
\Statex \textbf{Input:} ``Lookback'' window size $W$, Day ahead to predict $D$
\State Wait $W$ days to fill window.
\State Create model $f^W.init()$ \Com... | \begin{algorithm}
[h!]
\caption{Incremental learning for infection rates prediction}
\begin{algorithmic}
[1]
\Statex \textbf{Input:} ``Lookback'' window size $W$, Day ahead to predict $D$
\State Wait $W$ days to fill window.
\State Create model $f^W.init()$ \Comment $j = W$
\State Observe instance $\grave{\beta}^W = \{... | "https://arxiv.org/src/2401.06629" | "2401.06629.tar.gz" | "2024-01-12" | {
"title": "pandemic infection forecasting through compartmental model and learning-based approaches",
"id": "2401.06629",
"abstract": "the emergence and spread of deadly pandemics has repeatedly occurred throughout history, causing widespread infections and loss of life. the rapid spread of pandemics hav... | "2024-03-15T07:35:55.770135" | {
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} | [] | "algorithm" | "d94ccb53-8f69-4701-a9d6-76e2b9b651d1" | 1169 | hard | |
\begin{algorithm}\small
\caption{Approximating $\hat\sigma^2$}
\algorithmicrequire positive integers $N_\sigma$ and $N_\sigma^\prime$
\begin{algorithmic}[1]
\For{$l$ in 1 to $N_\sigma$}
\State draw subsample $\{ \iota_1,\dots, \iota_{n - D_\sigma} \}$ of size $n - D_\sigma$ without replacement from $\{ 1, \dots, n \... | \begin{algorithm}
\small
\caption{Approximating $\hat\sigma^2$}
\algorithmicrequire positive integers $N_\sigma$ and $N_\sigma^\prime$
\begin{algorithmic}
[1]
\For{$l$ in 1 to $N_\sigma$}
\State draw subsample $\{ \iota_1,\dots, \iota_{n - D_\sigma} \}$ of size $n - D_\sigma$ without replacement from $\{ 1, \dots, n \}... | "https://arxiv.org/src/2309.13251" | "2309.13251.tar.gz" | "2024-01-10" | {
"title": "nonparametric estimation of conditional densities by generalized random forests",
"id": "2309.13251",
"abstract": "considering a continuous random variable y together with a continuous random vector x, i propose a nonparametric estimator f^(.|x) for the conditional density of y given x=x. this... | "2024-03-15T06:26:55.263031" | {
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} | [] | "algorithm" | "736e8c64-df38-4b3c-9024-bc77f2a616e6" | 1265 | hard | |
\begin{algorithm}
\caption{Variable Selection (VS) with Momentum}
\begin{algorithmic}[1]
\State \textbf{Input}: iteration index $t$, $\mathcal{D}=\{(\mathbf{x}^{i},y^{i})\}_{i=1}^{t}$, $N_{init}$, $N_{vs}$, set of important variables chosen at iteration $t-N_{vs}$, denote as $\hat{\mathbf{x}}_{ipt}$
... | \begin{algorithm}
\caption{Variable Selection (VS) with Momentum}
\begin{algorithmic}
[1]
\State \textbf{Input}: iteration index $t$, $\mathcal{D}=\{(\mathbf{x}^{i},y^{i})\}_{i=1}^{t}$, $N_{init}$, $N_{vs}$, set of important variables chosen at iteration $t-N_{vs}$, denote as $\hat{\mathbf{x}}_{ipt}$
\State \textbf{Out... | "https://arxiv.org/src/2109.09264" | "2109.09264.tar.gz" | "2024-02-12" | {
"title": "computationally efficient high-dimensional bayesian optimization via variable selection",
"id": "2109.09264",
"abstract": "bayesian optimization (bo) is a method for globally optimizing black-box functions. while bo has been successfully applied to many scenarios, developing effective bo algor... | "2024-03-15T06:24:09.021286" | {
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} | [] | "algorithm" | "4a353026-90d1-45ab-b777-07b23417da66" | 913 | medium | |
\begin{algorithmic}[1]
\State{$ t \gets 1; \ \textbf{m}_{1} \gets \mathbf{1}_{\bar{C}}; \ \alpha \gets 1 $ \hfill \# Step t = 1}\label{start_t1:ln}
\For{$ e \gets 1,...,E $ }
\State{FBP() \hfill \# Algorithm~\ref{FBP:alg}}
\EndFor
\State{$ \bar{\textbf{s}}_{1} \gets \text{mean of} \ \tilde{s} \ \text{across data} $... | \begin{algorithmic}
[1]
\State{$ t \gets 1; \ \textbf{m}_{1} \gets \mathbf{1}_{\bar{C}}; \ \alpha \gets 1 $ \hfill \# Step t = 1}\For{$ e \gets 1,...,E $ }
\State{FBP() \hfill \# Algorithm~\ref{FBP:alg}}
\EndFor
\State{$ \bar{\textbf{s}}_{1} \gets \text{mean of} \ \tilde{s} \ \text{across data} $ }\For{$ t \gets 2,...,... | "https://arxiv.org/src/2210.06891" | "2210.06891.tar.gz" | "2024-02-23" | {
"title": "experimental design for multi-channel imaging via task-driven feature selection",
"id": "2210.06891",
"abstract": "this paper presents a data-driven, task-specific paradigm for experimental design, to shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices. cur... | "2024-03-15T03:14:35.065581" | {
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} | [] | "algorithm" | "87804314-b74f-4ac6-ad76-024ac9b40c57" | 1473 | hard | |
\begin{algorithm}[H]
\caption{Optimal Regress-later with Neural Networks (OPT-RLNN)}\label{alg-opt-rlnn}
\begin{algorithmic}[1]
\State Setup the target portfolio information (with strike $K$ and exercise points $\{t_0, t_1, t_2, \ldots, t_M\}$) and time-zero market data ($S_0$, $r$, $\sigma$)
\State Generate $S_{t_m}\l... | \begin{algorithm}
[H]
\caption{Optimal Regress-later with Neural Networks (OPT-RLNN)}\begin{algorithmic}
[1]
\State Setup the target portfolio information (with strike $K$ and exercise points $\{t_0, t_1, t_2, \ldots, t_M\}$) and time-zero market data ($S_0$, $r$, $\sigma$)
\State Generate $S_{t_m}\left(\omega_j\right)... | "https://arxiv.org/src/2402.15936" | "2402.15936.tar.gz" | "2024-02-24" | {
"title": "optimizing neural networks for bermudan option pricing: convergence acceleration, future exposure evaluation and interpolation in counterparty credit risk",
"id": "2402.15936",
"abstract": "this paper presents a monte-carlo-based artificial neural network framework for pricing bermudan optio... | "2024-03-15T02:36:57.519067" | {
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} | [] | "algorithm" | "bfdafd59-4b11-4acc-9e7b-2917dca5f767" | 1643 | hard | |
\begin{algorithm}
\caption{The individual stock return fitting step in training GF-AGRU (for stock $i$).} \label{algorithm_i}
\textbf{Hyperparameters}: the same as in Algorithm \ref{algorithm_N}.\\
\textbf{Input}: training data including the features $F_{<t}$ and the label $Y_i^t$. $F_{<t}$ is constructed b... | \begin{algorithm}
\caption{The individual stock return fitting step in training GF-AGRU (for stock $i$).} \textbf{Hyperparameters}: the same as in Algorithm \ref{algorithm_N}.\\
\textbf{Input}: training data including the features $F_{<t}$ and the label $Y_i^t$. $F_{<t}$ is constructed by concatenating historical daily... | "https://arxiv.org/src/2301.07318" | "2301.07318.tar.gz" | "2024-01-16" | {
"title": "dynamic cvar portfolio construction with attention-powered generative factor learning",
"id": "2301.07318",
"abstract": "the dynamic portfolio construction problem requires dynamic modeling of the joint distribution of multivariate stock returns. to achieve this, we propose a dynamic generativ... | "2024-03-15T06:00:53.804038" | {
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} | [] | "algorithm" | "61c70b9d-2569-4597-a145-631b9b0bd5f6" | 2171 | hard | |
\begin{algorithmic}
\Require $L,\ell,\gamma_k$
\Ensure $\theta_k, \gamma_{k+1}$
\State Solve $L\theta_k^2+(\gamma_k-\ell)\theta_k-\gamma_k=0$ via
the quadratic formula for the positive root $\theta_k$.
\State Let $\gamma_{k+1}:=(1-\theta_k)\gamma_k+\theta_k\ell$
\end{algorithmic}
| \begin{algorithmic}
\Require $L,\ell,\gamma_k$
\Ensure $\theta_k, \gamma_{k+1}$
\State Solve $L\theta_k^2+(\gamma_k-\ell)\theta_k-\gamma_k=0$ via
the quadratic formula for the positive root $\theta_k$.
\State Let $\gamma_{k+1}:=(1-\theta_k)\gamma_k+\theta_k\ell$
\end{algorithmic} | "https://arxiv.org/src/2111.11613" | "2111.11613.tar.gz" | "2024-01-03" | {
"title": "nonlinear conjugate gradient for smooth convex functions",
"id": "2111.11613",
"abstract": "the method of nonlinear conjugate gradients (ncg) is widely used in practice for unconstrained optimization, but it satisfies weak complexity bounds at best when applied to smooth convex functions. in con... | "2024-03-15T07:02:13.702050" | {
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} | [] | "algorithm" | "ab9e74ea-091f-49c0-b47a-a97fd4a2ad96" | 280 | easy | |
\begin{algorithm}
\caption{MAP-EM algorithm for the computation of $\hat{\mathbf{c}}$ in \eqref{computec}}
\begin{algorithmic}[1]
\State Select an initial estimate $\hat{\mathbf{c}}^{(1)}$, a maximum number of iterations $M_{\textnormal{iter}}$, and a tolerance factor $\epsilon$
\State $j\gets 1$, $\textn... | \begin{algorithm}
\caption{MAP-EM algorithm for the computation of $\hat{\mathbf{c}}$ in \eqref{computec}}
\begin{algorithmic}
[1]
\State Select an initial estimate $\hat{\mathbf{c}}^{(1)}$, a maximum number of iterations $M_{\textnormal{iter}}$, and a tolerance factor $\epsilon$
\State $j\gets 1$, $\textnormal{flag}\g... | "https://arxiv.org/src/2303.06045" | "2303.06045.tar.gz" | "2024-02-23" | {
"title": "kernel-based identification using lebesgue-sampled data",
"id": "2303.06045",
"abstract": "sampling in control applications is increasingly done non-equidistantly in time. this includes applications in motion control, networked control, resource-aware control, and event-based control. some of th... | "2024-03-15T03:38:24.648127" | {
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} | [] | "algorithm" | "b83c2725-73c7-4a20-aaee-e87c21eff5ce" | 1334 | hard | |
\begin{algorithm}[H]
\caption{Approximate Minimal Sub-Cover}
\label{alg:AMSC}
\begin{algorithmic}[1]
\State Set $G_\text{now} = \vec{G}(C_i)$.
\While{$G_\text{now}$ has at least one vertex}
\State Add the vertex $v^*$ with the largest out-degree in $G_\text{now}$ and its corresponding radius to the domi... | \begin{algorithm}
[H]
\caption{Approximate Minimal Sub-Cover}
\begin{algorithmic}
[1]
\State Set $G_\text{now} = \vec{G}(C_i)$.
\While{$G_\text{now}$ has at least one vertex}
\State Add the vertex $v^*$ with the largest out-degree in $G_\text{now}$ and its corresponding radius to the dominating set $C_i^*$.
\State Remo... | "https://arxiv.org/src/2301.09734" | "2301.09734.tar.gz" | "2024-02-08" | {
"title": "topological learning in multi-class data sets",
"id": "2301.09734",
"abstract": "we specialize techniques from topological data analysis to the problem of characterizing the topological complexity (as defined in the body of the paper) of a multi-class data set. as a by-product, a topological cla... | "2024-03-15T07:16:13.009881" | {
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} | [] | "algorithm" | "84df6c7b-73a3-4b5c-8522-effeda62a386" | 411 | easy | |
\begin{algorithm}[H]
\centering
\small
\caption{SNN Index}\label{algo:index}
\begin{algorithmic}[1]
\State \textbf{Input:} Data matrix $P=[p_1,p_2,\ldots,p_n]^T \in \mathbb{R}^{n \times d}$
\State Compute $\mu := \mathrm{mean}(\{p_j\})$
\State Compute the mean-centered matrix $X$ with rows $... | \begin{algorithm}
[H]
\centering
\small
\caption{SNN Index} \begin{algorithmic}
[1]
\State \textbf{Input:} Data matrix $P=[p_1,p_2,\ldots,p_n]^T \in \mathbb{R}^{n \times d}$
\State Compute $\mu := \mathrm{mean}(\{p_j\})$
\State Compute the mean-centered matrix $X$ with rows $x_i:= p_i - \mu$
\State Compute the singular... | "https://arxiv.org/src/2212.07679" | "2212.07679.tar.gz" | "2024-01-29" | {
"title": "fast and exact fixed-radius neighbor search based on sorting",
"id": "2212.07679",
"abstract": "fixed-radius near neighbor search is a fundamental data operation that retrieves all data points within a user-specified distance to a query point. there are efficient algorithms that can provide fast... | "2024-03-15T08:42:59.915070" | {
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} | [] | "algorithm" | "ffa00ef9-6d6e-4f52-8d10-7662be803134" | 700 | easy | |
\begin{algorithmic}
\Require Data \(\{t^{(i)}, \hat{\mathbf{u}}^{(i)}\}_{i=0}^N\), untrained model \(\mathbf{U}(...; \boldsymbol{\theta})\)\\
\Return Coupling strength(\(k\)), homotopy parameter decrement ratio(\(\kappa\)), number of homotopy steps(\(s\)), epochs per homotopy step(\(n_{epoch}\)), learning rate(\(\eta\)... | \begin{algorithmic}
\Require Data \(\{t^{(i)}, \hat{\mathbf{u}}^{(i)}\}_{i=0}^N\), untrained model \(\mathbf{U}(...; \boldsymbol{\theta})\)\\
\Return Coupling strength(\(k\)), homotopy parameter decrement ratio(\(\kappa\)), number of homotopy steps(\(s\)), epochs per homotopy step(\(n_{epoch}\)), learning rate(\(\eta\)... | "https://arxiv.org/src/2210.01407" | "2210.01407.tar.gz" | "2024-01-23" | {
"title": "homotopy-based training of neuralodes for accurate dynamics discovery",
"id": "2210.01407",
"abstract": "neural ordinary differential equations (neuralodes) present an attractive way to extract dynamical laws from time series data, as they bridge neural networks with the differential equation-ba... | "2024-03-15T07:14:36.473195" | {
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} | [] | "algorithm" | "6b1faeaf-94f1-4b8b-9f17-a0e1d72ca38a" | 1942 | hard | |
\begin{algorithmic}[1]
\State Initialize the weights of an RL agent and a Self-Explainer (SE-Net)
\State Initialize buffers $\mathcal{D}_{success}$ and $\mathcal{D}_{failure}$ for training the Self-Explainer and $D_{RL}$ for RL from Demonstrations as in the RLfD works \cite{hester2017deep,vecerik2017leveragi... | \begin{algorithmic}[1]
\State Initialize the weights of an RL agent and a Self-Explainer (SE-Net)
\State Initialize buffers $\mathcal{D}_{success}$ and $\mathcal{D}_{failure}$ for training the Self-Explainer and $D_{RL}$ for RL from Demonstrations as in the RLfD works \cite{hester2017deep,vecerik2017leveraging}
\State ... | "https://arxiv.org/src/2110.05286" | "2110.05286.tar.gz" | "2024-02-07" | {
"title": "learning from ambiguous demonstrations with self-explanation guided reinforcement learning",
"id": "2110.05286",
"abstract": "our work aims at efficiently leveraging ambiguous demonstrations for the training of a reinforcement learning (rl) agent. an ambiguous demonstration can usually be inte... | "2024-03-15T07:11:54.770777" | {
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} | [] | "algorithm" | "9c262d00-9dd9-4757-8546-807d775a66cf" | 1933 | hard | |
\begin{algorithmic}[1]
\State Choose a proper filter function $f$ to project data on the real line, $f: X \rightarrow \mathbb{R}$.
\State Choose a component number $n$ and overlap percentage ratio $p$.
\State Construct a cover $\mathcal{U} = (u_i), i=1...n$ on projected data $f(X)$ based on the pa... | \begin{algorithmic}
[1]
\State Choose a proper filter function $f$ to project data on the real line, $f: X \rightarrow \mathbb{R}$.
\State Choose a component number $n$ and overlap percentage ratio $p$.
\State Construct a cover $\mathcal{U} = (u_i), i=1...n$ on projected data $f(X)$ based on the parameter $n$ and $p$.
... | "https://arxiv.org/src/2401.12237" | "2401.12237.tar.gz" | "2024-01-19" | {
"title": "a distribution-guided mapper algorithm",
"id": "2401.12237",
"abstract": "motivation: the mapper algorithm is an essential tool to explore shape of data in topology data analysis. with a dataset as an input, the mapper algorithm outputs a graph representing the topological features of the whole ... | "2024-03-15T06:54:42.856784" | {
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} | [] | "algorithm" | "f611d244-1f76-4ec1-b58e-03a6c29d6bcb" | 495 | easy | |
\begin{algorithm}
\caption{}\label{alg2}
\begin{algorithmic}[1]
\State Let $\mathcal{J}=\{I_1,I_2,\ldots,I_m\}$ be the set of subintervals formed the chore $[0,1]$.
\State Solve the following linear program:
\begin{align}\label{eq1}
\min \quad
& \sum_{i,j =1}^{n} \sum_{k=1}^m x_{j,I_k} V_{i,j}(I_k)
\end{align}
s.t.
... | \begin{algorithm}
\caption{}\begin{algorithmic}
[1]
\State Let $\mathcal{J}=\{I_1,I_2,\ldots,I_m\}$ be the set of subintervals formed the chore $[0,1]$.
\State Solve the following linear program:
\begin{align*}
\min \quad
& \sum_{i,j =1}^{n} \sum_{k=1}^m x_{j,I_k} V_{i,j}(I_k)
\end{align*}
s.t.
\begin{align*}
\sum_{i=1... | "https://arxiv.org/src/2303.12446" | "2303.12446.tar.gz" | "2024-02-24" | {
"title": "externalities in chore division",
"id": "2303.12446",
"abstract": "the chore division problem simulates the fair division of a heterogeneous, undesirable resource among several agents. in the fair division of chores, each agent only gets the disutility from its own piece. agents may, however, al... | "2024-03-15T03:42:06.657255" | {
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} | [] | "algorithm" | "a4e77adb-9d77-484f-b875-345aa06ccf95" | 892 | medium | |
\begin{algorithmic}
\Require{$\{M, x_0, x_M, r,\lambda_t, D_{\alpha}, \alpha, \nu, \beta, \lambda, \mu\}$ (Table \ref{tab:parameters})}
\State{1: Compute Time Steps: $\{\Delta t_k\}_{k=1}^M$ and $\Delta t$}
\State{2: Compute State Steps: $\{ \Delta x_k \}_{k=1}^M$ and $\Delta x$ (Eqn. [\ref{eq:latticesize}]) }
\State{... | \begin{algorithmic}
\Require{$\{M, x_0, x_M, r,\lambda_t, D_{\alpha}, \alpha, \nu, \beta, \lambda, \mu\}$ (Table \ref{tab:parameters})}
\State{1: Compute Time Steps: $\{\Delta t_k\}_{k=1}^M$ and $\Delta t$}
\State{2: Compute State Steps: $\{ \Delta x_k \}_{k=1}^M$ and $\Delta x$ (Eqn. [\ref{eq:latticesize}]) }
\State{3... | "https://arxiv.org/src/2310.06079" | "2310.06079.tar.gz" | "2024-01-16" | {
"title": "anomalous diffusion and price impact in the fluid-limit of an order book",
"id": "2310.06079",
"abstract": "we extend a discrete time random walk (dtrw) numerical scheme to simulate the anomalous diffusion of financial market orders in a simulated order book. here using random walks with sibuya ... | "2024-03-15T06:07:24.790122" | {
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} | [] | "algorithm" | "9ab15a52-eaff-4cae-8077-1df3ee5e394c" | 1013 | medium | |
\begin{algorithmic}[1]
\State Input: Two probability distributions $\P_B,\P_R$ supported on $B,R\subset Q_d$, and a tilt factor $\alpha\in(0,1)$.
\State Output: Probability distribution $\P_B'$ supported on $B$. $\P_B'$ is close to $\alpha\P_R+(1-\alpha)\P_B$ in $W_1$, under assumptions of Theorem~\ref{main1:thm}.
\For... | \begin{algorithmic}
[1]
\State Input: Two probability distributions $\P_B,\P_R$ supported on $B,R\subset Q_d$, and a tilt factor $\alpha\in(0,1)$.
\State Output: Probability distribution $\P_B'$ supported on $B$. $\P_B'$ is close to $\alpha\P_R+(1-\alpha)\P_B$ in $W_1$, under assumptions of Theorem~\ref{main1:thm}.
\Fo... | "https://arxiv.org/src/2401.11562" | "2401.11562.tar.gz" | "2024-01-21" | {
"title": "enhancing selectivity using wasserstein distance based reweighing",
"id": "2401.11562",
"abstract": "given two labeled data-sets $\\mathcal{s}$ and $\\mathcal{t}$, we design a simple and efficient greedy algorithm to reweigh the loss function such that the limiting distribution of the neural net... | "2024-03-15T07:03:20.799704" | {
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} | [] | "algorithm" | "c391ee7b-2e5f-4597-8dea-02a54f2af3df" | 987 | medium | |
\begin{algorithm}[t]
\caption{\textbf{SplitSGD}($\eta, w, l, q, B, t_1, \theta_0, \gamma$)}
\label{procedure}
{\fontsize{10}{15} \selectfont
\begin{algorithmic}[1]
\State $\eta_1 = \eta$
\State $\theta_1^{in} = \theta_0$
\For{$b = 1,..., B$}
\State Run SGD with constant step size $\eta_b$ for $t_b$ steps, starting from... | \begin{algorithm}
[t]
\caption{\textbf{SplitSGD}($\eta, w, l, q, B, t_1, \theta_0, \gamma$)}
{\fontsize{10}{15} \selectfont
\begin{algorithmic}
[1]
\State $\eta_1 = \eta$
\State $\theta_1^{in} = \theta_0$
\For{$b = 1,..., B$}
\State Run SGD with constant step size $\eta_b$ for $t_b$ steps, starting from $\theta_{b}^{in... | "https://arxiv.org/src/1910.08597" | "1910.08597.tar.gz" | "2024-02-16" | {
"title": "robust learning rate selection for stochastic optimization via splitting diagnostic",
"id": "1910.08597",
"abstract": "this paper proposes splitsgd, a new dynamic learning rate schedule for stochastic optimization. this method decreases the learning rate for better adaptation to the local geom... | "2024-03-15T04:35:48.772484" | {
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} | [] | "algorithm" | "bb78be88-561e-4048-a126-61b45570103a" | 693 | easy | |
\begin{algorithm}[!ht]
\caption{3d Subspace Intersects Box}
\begin{algorithmic}
\Require
\State \hspace{5mm}Boundary $\mathcal{S} = [-B,B[\times[-B,B[\times[-B,B[\times[-B,B[$
\State \hspace{5mm}4d normal $N=(n_x,n_y,n_z,n_t)$ to 3d subspace $\mathcal{S_N}$
\State \hspace{5mm}Point $V=(x,y,z,t)$ contained in $\... | \begin{algorithm}
[!ht]
\caption{3d Subspace Intersects Box}
\begin{algorithmic}
\Require
\State \hspace{5mm}Boundary $\mathcal{S} = [-B,B[\times[-B,B[\times[-B,B[\times[-B,B[$
\State \hspace{5mm}4d normal $N=(n_x,n_y,n_z,n_t)$ to 3d subspace $\mathcal{S_N}$
\State \hspace{5mm}Point $V=(x,y,z,t)$ contained in $\mathcal... | "https://arxiv.org/src/2212.04999" | "2212.04999.tar.gz" | "2024-02-06" | {
"title": "an implementation of the extended tower number field sieve using 4d sieving in a box and a record computation in fp4",
"id": "2212.04999",
"abstract": "we report on an implementation of the extended tower number field sieve (extnfs) and record computation in a medium characteristic finite fiel... | "2024-03-15T07:40:23.873860" | {
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} | [] | "algorithm" | "e1a0de20-c044-4ffb-b88b-b19504ea14fa" | 1631 | hard | |
\begin{algorithm}[H]
\caption{ TADRED Forward \& Backward Pass (FBP) in Step $ t $}\label{FBP:alg}
\textbf{Requires:}
\\ Input and Target Data $ X_{\bar{D}}, Y $, Mask $ \textbf{m}_{t} $
\\ Scoring and Task Networks $ \mathcal{S}_{t}, \mathcal{T}_{t} $, Loss $ L $
\\ Sample-independent Feature Score $ \bar{\textbf{s}}... | \begin{algorithm}
[H]
\caption{ TADRED Forward \& Backward Pass (FBP) in Step $ t $}\textbf{Requires:}
\\ Input and Target Data $ X_{\bar{D}}, Y $, Mask $ \textbf{m}_{t} $
\\ Scoring and Task Networks $ \mathcal{S}_{t}, \mathcal{T}_{t} $, Loss $ L $
\\ Sample-independent Feature Score $ \bar{\textbf{s}}_{t} $
\\ Mix Pa... | "https://arxiv.org/src/2210.06891" | "2210.06891.tar.gz" | "2024-02-23" | {
"title": "experimental design for multi-channel imaging via task-driven feature selection",
"id": "2210.06891",
"abstract": "this paper presents a data-driven, task-specific paradigm for experimental design, to shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices. cur... | "2024-03-15T03:59:49.884455" | {
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} | [] | "algorithm" | "9f78d3a2-6b6a-4033-bf41-5cd0dccc765e" | 894 | medium | |
\begin{algorithmic}[1]
\State possible pair list $\gets$ conventional neighbour list
\State new pair list $\gets \varnothing$
\ForAll{stickers $i$}
\State $N_{bonds}[i]\gets0$
\EndFor
\State shuffle possible pair list
\ForAll{pairs $(i,j) \in$ possible pair list}
\State $\Delta E_{ij} \gets U_{bound}(r_{ij})-U_... | \begin{algorithmic}
[1]
\State possible pair list $\gets$ conventional neighbour list
\State new pair list $\gets \varnothing$
\ForAll{stickers $i$}
\State $N_{bonds}[i]\gets0$
\EndFor
\State shuffle possible pair list
\ForAll{pairs $(i,j) \in$ possible pair list}
\State $\Delta E_{ij} \gets U_{bound}(r_{ij})-U_{unboun... | "https://arxiv.org/src/2302.13623" | "2302.13623.tar.gz" | "2024-02-10" | {
"title": "evanescent gels: competition between sticker dynamics and single chain relaxation",
"id": "2302.13623",
"abstract": "solutions of polymer chains are modelled using non-equilibrium brownian dynamics simulations, with physically associative beads which form reversible crosslinks to establish a s... | "2024-03-15T05:06:25.025312" | {
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} | [] | "algorithm" | "33c72b1d-a532-4c5d-9e1a-c090a0bd9c84" | 874 | medium | |
\begin{algorithm}
\caption{Synthetic bid validation / Double validation}
\begin{algorithmic}[1]
\State $\beta \gets \beta^*$ \Comment{load best set of parameters for BidNet}
\State $\alpha \gets \alpha^*$ \Comment{load optimized set of parameters for synthesizer}
\State $\tilde{\mathbf{c}}\s... | \begin{algorithm}
\caption{Synthetic bid validation / Double validation}
\begin{algorithmic}
[1]
\State $\beta \gets \beta^*$ \Comment{load best set of parameters for BidNet}
\State $\alpha \gets \alpha^*$ \Comment{load optimized set of parameters for synthesizer}
\State $\tilde{\mathbf{c}}\sim A_{\alpha^*}(\mathbf{z})... | "https://arxiv.org/src/2207.12255" | "2207.12255.tar.gz" | "2024-02-15" | {
"title": "implementing a hierarchical deep learning approach for simulating multi-level auction data",
"id": "2207.12255",
"abstract": "we present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. the complexities encountered in thi... | "2024-03-15T04:00:57.383729" | {
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} | [] | "algorithm" | "9534119b-ef69-4ff8-936a-b1ab80205947" | 1153 | medium | |
\begin{algorithmic}[1]
\State {\bf data structure} \textsc{LSH}
\State {\bf members}
\State \hspace{4mm} $d,n \in \mathbb{N}_+$ \Comment{$d$ is dimension, $n$ is number of data points}
\State \hspace{4mm} $K,L\in \mathbb{N}_+$ \Comment{$K$ is amplification factor, $L$ is number of repetition for hashing}
\S... | \begin{algorithmic}
[1]
\State {\bf data structure} \textsc{LSH}
\State {\bf members}
\State \hspace{4mm} $d,n \in \mathbb{N}_+$ \Comment{$d$ is dimension, $n$ is number of data points}
\State \hspace{4mm} $K,L\in \mathbb{N}_+$ \Comment{$K$ is amplification factor, $L$ is number of repetition for hashing}
\State \hspac... | "https://arxiv.org/src/2208.03915" | "2208.03915.tar.gz" | "2024-02-13" | {
"title": "dynamic maintenance of kernel density estimation data structure: from practice to theory",
"id": "2208.03915",
"abstract": "kernel density estimation (kde) stands out as a challenging task in machine learning. the problem is defined in the following way: given a kernel function $f(x,y)$ and a ... | "2024-03-15T05:43:39.046127" | {
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} | [] | "algorithm" | "87fe858d-35b2-4465-ba95-37fbdc8131e7" | 1925 | hard | |
\begin{algorithm}
\caption{Penalized G-estimation algorithm}\label{penG.algorithm}
\begin{algorithmic}[1]
\Procedure{penalizedG}{$\vec A, \vec H, \vec Y, \lambda$,\,corstr,\,$\kappa$} %Put comment if you want: \Comment{The g.c.d. of a and b}
\State Compute $E(\vec A_i|\vec H_i)$ for $i=1,\ldots,n$ \Comment{logistic... | \begin{algorithm}
\caption{Penalized G-estimation algorithm}\begin{algorithmic}
[1]
\Procedure{penalizedG}{$\vec A, \vec H, \vec Y, \lambda$,\,corstr,\,$\kappa$} %Put comment if you want: \Comment{The g.c.d. of a and b}
\State Compute $E(\vec A_i|\vec H_i)$ for $i=1,\ldots,n$ \Comment{logistic regression on the pooled ... | "https://arxiv.org/src/2402.00154" | "2402.00154.tar.gz" | "2024-02-16" | {
"title": "penalized g-estimation for effect modifier selection in a structural nested mean model for repeated outcomes",
"id": "2402.00154",
"abstract": "effect modification occurs when the impact of the treatment on an outcome varies based on the levels of other covariates known as effect modifiers. mo... | "2024-03-15T05:18:15.887063" | {
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} | [] | "algorithm" | "24758dfb-f2c5-491c-a6b7-16eac0570e33" | 1938 | hard | |
\begin{algorithmic}[1]
\While{$\mbox{bias}(\hat{\theta}_{0,p}, \tilde{\theta}_{b,p}) > 0.3 \times S_{p}$, for all $p$}
\State Sample $\theta_{n,p} \sim \mbox{Unif}(a_{1,p}, a_{2,p}), n = 1, \ldots, N$
\State Simulate $\mathbf{x}_n^* \sim p(;\boldsymbol{\theta}_n), n = 1, \ldots, N$
\State Trai... | \begin{algorithmic}
[1]
\While{$\mbox{bias}(\hat{\theta}_{0,p}, \tilde{\theta}_{b,p}) > 0.3 \times S_{p}$, for all $p$}
\State Sample $\theta_{n,p} \sim \mbox{Unif}(a_{1,p}, a_{2,p}), n = 1, \ldots, N$
\State Simulate $\mathbf{x}_n^* \sim p(;\boldsymbol{\theta}_n), n = 1, \ldots, N$
\State Train $\mathcal{F}_{\phi}(\ma... | "https://arxiv.org/src/2303.15041" | "2303.15041.tar.gz" | "2024-02-19" | {
"title": "towards black-box parameter estimation",
"id": "2303.15041",
"abstract": "deep learning algorithms have recently shown to be a successful tool in estimating parameters of statistical models for which simulation is easy, but likelihood computation is challenging. but the success of these approach... | "2024-03-15T05:01:49.289931" | {
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} | [] | "algorithm" | "c8268520-098b-4a66-8404-827a27550013" | 1029 | medium | |
\begin{algorithmic}[1]
\State
Set $\mathcal{D} \gets \emptyset$ \Comment{{\it Initialize dataset.}}
\For{$t=1,\ldots, T$} \Comment{{\it Training $T$ rounds}}
\State $\beta_1,\ldots,\beta_M \sim P_{\text{exp}}(\beta)$ \Comment{{\it Sample temperatures from exploration query prior.}}
\For{$m=1,\ldots,M$}
\St... | \begin{algorithmic}
[1]
\State
Set $\mathcal{D} \gets \emptyset$ \Comment{{\it Initialize dataset.}}
\For{$t=1,\ldots, T$} \Comment{{\it Training $T$ rounds}}
\State $\beta_1,\ldots,\beta_M \sim P_{\text{exp}}(\beta)$ \Comment{{\it Sample temperatures from exploration query prior.}}
\For{$m=1,\ldots,M$}
\State $\tau_m ... | "https://arxiv.org/src/2310.02823" | "2310.02823.tar.gz" | "2024-02-04" | {
"title": "learning to scale logits for temperature-conditional gflownets",
"id": "2310.02823",
"abstract": "gflownets are probabilistic models that sequentially generate compositional structures through a stochastic policy. among gflownets, temperature-conditional gflownets can introduce temperature-based... | "2024-03-15T07:30:57.250575" | {
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} | [] | "algorithm" | "ede6d8f2-a767-4b4f-8a0c-91464d30530a" | 697 | easy | |
\begin{algorithmic}
\Function{Transport}{$\bold{M}, \boldsymbol{\beta},\bold{g}, \Delta t$} \Comment{Input moments, natural parameters, and gauge parameters for all cells}
\State $\boldsymbol{\beta}\gets \Call{NewtonOptimization}{\bold{M},\boldsymbol{\beta}, \boldsymbol{\phi}(\bold{u} ;\mathbf{g})}$ \Comment{Solve the ... | \begin{algorithmic}
\Function{Transport}{$\bold{M}, \boldsymbol{\beta},\bold{g}, \Delta t$} \Comment{Input moments, natural parameters, and gauge parameters for all cells}
\State $\boldsymbol{\beta}\gets \Call{NewtonOptimization}{\bold{M},\boldsymbol{\beta}, \boldsymbol{\phi}(\bold{u} ;\mathbf{g})}$ \Comment{Solve the ... | "https://arxiv.org/src/2303.02898" | "2303.02898.tar.gz" | "2024-02-19" | {
"title": "stabilizing the maximal entropy moment method for rarefied gas dynamics at single-precision",
"id": "2303.02898",
"abstract": "the maximal entropy moment method (mem) is systematic solution of the challenging problem: generating extended hydrodynamic equations valid for both dense and rarefied... | "2024-03-15T03:56:31.323537" | {
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} | [] | "algorithm" | "c7b25e2d-eada-4336-adc6-ccf52d67f217" | 2664 | hard | |
\begin{algorithmic}[1]
\State Inputs: $M, K, \epsilon, \mathcal{P}$ (the page dataset)
\State $H_P := \varnothing, \forall P\in \mathcal{P}$
\State $\mathrm{nn\_model} := \mathrm{model\_init}()$
\For {$epoch = 1,2,\ldots$}
\State $S_{train} := \varnothing$
\For {\textbf{each} webpage $P \in \mathcal{P}$}... | \begin{algorithmic}
[1]
\State Inputs: $M, K, \epsilon, \mathcal{P}$ (the page dataset)
\State $H_P := \varnothing, \forall P\in \mathcal{P}$
\State $\mathrm{nn\_model} := \mathrm{model\_init}()$
\For {$epoch = 1,2,\ldots$}
\State $S_{train} := \varnothing$
\For {\textbf{each} webpage $P \in \mathcal{P}$}
\State $P\tri... | "https://arxiv.org/src/2111.02168" | "2111.02168.tar.gz" | "2024-02-23" | {
"title": "the klarna product page dataset: web element nomination with graph neural networks and large language models",
"id": "2111.02168",
"abstract": "web automation holds the potential to revolutionize how users interact with the digital world, offering unparalleled assistance and simplifying tasks ... | "2024-03-15T02:50:08.214851" | {
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"insult_score": 0.009127886,
... | {
"num_done": {
"equation": 3,
"table": 0,
"figure": 0,
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"plot": 1
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} | [] | "algorithm" | "79f588ba-e2cf-48bd-9b7a-8b1338fded12" | 868 | medium | |
\begin{algorithmic}[1]
\Require Observations, history $d_{1:2} = (d_1, d_2)$, {\tt model} $\in$ {\tt \{full interactions, linear\}}.
\If{{\tt model} $=$ {\tt full interactions}}
\State Estimate $\beta_{d_{1:2}}^{(2)} $ by regressing $Y_{i,2}$ onto $H_{i,2}$ for all $i: (D_{i, 1:2} = d_{1:2})$;
\St... | \begin{algorithmic}
[1]
\Require Observations, history $d_{1:2} = (d_1, d_2)$, {\tt model} $\in$ {\tt \{full interactions, linear\}}.
\If{{\tt model} $=$ {\tt full interactions}}
\State Estimate $\beta_{d_{1:2}}^{(2)} $ by regressing $Y_{i,2}$ onto $H_{i,2}$ for all $i: (D_{i, 1:2} = d_{1:2})$;
\State Estimate $\beta_{... | "https://arxiv.org/src/2103.01280" | "2103.01280.tar.gz" | "2024-01-26" | {
"title": "dynamic covariate balancing: estimating treatment effects over time with potential local projections",
"id": "2103.01280",
"abstract": "this paper studies the estimation and inference of treatment histories in panel data settings when treatments change dynamically over time. we propose a met... | "2024-03-15T05:21:52.236887" | {
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... | {
"num_done": {
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} | [] | "algorithm" | "8775e21c-b899-489a-b60e-4c9afec1f0ef" | 951 | medium | |
\begin{algorithm}[h]
\caption{Particle Filter}
\label{alg:pf}
\begin{enumerate}
\item{Input: data $y_{1:T}$, level $l\in\mathbb{N}_0$, particle number $N\in\mathbb{N}$ and parameter $\theta\in\Theta$.}
\item{Initialize: For $i\in\{1,\dots,N\}$, independently generate $\overline{W}_{\Delta_l:1}^i$ from $\mathcal{N}(0,\D... | \begin{algorithm}
[h]
\caption{Particle Filter}
\begin{enumerate}
\item{Input: data $y_{1:T}$, level $l\in\mathbb{N}_0$, particle number $N\in\mathbb{N}$ and parameter $\theta\in\Theta$.}
\item{Initialize: For $i\in\{1,\dots,N\}$, independently generate $\overline{W}_{\Delta_l:1}^i$ from $\mathcal{N}(0,\Delta_l)$. Set ... | "https://arxiv.org/src/2310.03114" | "2310.03114.tar.gz" | "2024-02-19" | {
"title": "bayesian parameter inference for partially observed stochastic volterra equations",
"id": "2310.03114",
"abstract": "in this article we consider bayesian parameter inference for a type of partially observed stochastic volterra equation (sve). sves are found in many areas such as physics and ma... | "2024-03-15T05:09:03.161347" | {
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"insult_score": 0.0075128763,
"... | {
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"figure": 0,
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} | [] | "algorithm" | "1659b1f9-da27-461f-82a7-7289e2470234" | 1521 | hard | |
\begin{algorithmic}[1]
\Require{$\{(A_i,Y_i)\}_{i=1}^m$ , $\delta \in (0,1)$}{}
\State Set $k=1$, and $U_k = V$
\While{$|U_k|>1$}
\For{$u \in U_k$ }
\State $X_i=A_{i}(U_{k})[u,\cdot]$
\State $\beta(u) = Dcor(\{X_i,Y_i\}_{i=1}^m )$
\EndFor
\State Set $t$ be the $\delta$ quantile among $\{\beta(u), u \in... | \begin{algorithmic}
[1]
\Require{$\{(A_i,Y_i)\}_{i=1}^m$ , $\delta \in (0,1)$}{}
\State Set $k=1$, and $U_k = V$
\While{$|U_k|>1$}
\For{$u \in U_k$ }
\State $X_i=A_{i}(U_{k})[u,\cdot]$
\State $\beta(u) = Dcor(\{X_i,Y_i\}_{i=1}^m )$
\EndFor
\State Set $t$ be the $\delta$ quantile among $\{\beta(u), u \in U_k\}$
\State S... | "https://arxiv.org/src/1801.07683" | "1801.07683.tar.gz" | "2024-02-05" | {
"title": "discovering the signal subgraph: an iterative screening approach on graphs",
"id": "1801.07683",
"abstract": "supervised learning on graphs is a challenging task due to the high dimensionality and inherent structural dependencies in the data, where each edge depends on a pair of vertices. exis... | "2024-03-15T06:55:05.801196" | {
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"insult_score": 0.007531876,
"profanit... | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
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} | [] | "algorithm" | "982d67a9-cb83-40df-81dd-501d13c15bf7" | 534 | easy | |
\begin{algorithm}[t]
\small
\caption{On-the-fly DA Denoising (ODDA)}\label{alg:alg}
\begin{tabular}{p{2em}p{20em}}
\textbf{Input:} & Teacher model $f_T(\cdot)$, student model $f(\cdot)$, original dataset $\mathcal{D}=\{(x_i, y_i)\}, i=1,\cdots,n$, augmented dataset $\mathcal{D}'=\{(x_i', y_i')\}, i=1,\cdots,kn$, OD tem... | \begin{algorithm}
[t]
\small
\caption{On-the-fly DA Denoising (ODDA)}\begin{tabular}
{p{2em}p{20em}}
\textbf{Input:} & Teacher model $f_T(\cdot)$, student model $f(\cdot)$, original dataset $\mathcal{D}=\{(x_i, y_i)\}, i=1,\cdots,n$, augmented dataset $\mathcal{D}'=\{(x_i', y_i')\}, i=1,\cdots,kn$, OD temperature $\tau... | "https://arxiv.org/src/2212.10558" | "2212.10558.tar.gz" | "2024-01-31" | {
"title": "on-the-fly denoising for data augmentation in natural language understanding",
"id": "2212.10558",
"abstract": "data augmentation (da) is frequently used to provide additional training data without extra human annotation automatically. however, data augmentation may introduce noisy data that i... | "2024-03-15T08:24:23.744468" | {
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... | {
"num_done": {
"figure": 0,
"algorithm": 2
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} | [] | "algorithm" | "0b2cd615-032c-4ee5-8359-9d6c4bc16d55" | 1226 | hard | |
\begin{algorithm}
\caption{Algorithm for detecting equilibrium}\label{algo2}
\begin{algorithmic}[1]
\State Initialize an empty list $l$
\For{\textbf{each} $i$ \textbf{in} $producers$}
\For{$t$ \textbf{in} $[500, 900)$}
\If{$\forall x \in \{p_{it},p_{i(t+1)},\dots,p_{i1000}\}(x < \epsilon)$}
\State $... | \begin{algorithm}
\caption{Algorithm for detecting equilibrium}\begin{algorithmic}
[1]
\State Initialize an empty list $l$
\For{\textbf{each} $i$ \textbf{in} $producers$}
\For{$t$ \textbf{in} $[500, 900)$}
\If{$\forall x \in \{p_{it},p_{i(t+1)},\dots,p_{i1000}\}(x < \epsilon)$}
\State $l$\textbf{.push}(True)
\State \te... | "https://arxiv.org/src/2401.07070" | "2401.07070.tar.gz" | "2024-01-13" | {
"title": "a dynamic agent based model of the real economy with monopolistic competition, perfect product differentiation, heterogeneous agents, increasing returns to scale and trade in disequilibrium",
"id": "2401.07070",
"abstract": "we have used agent-based modeling as our numerical method to artifi... | "2024-03-15T06:13:08.276479" | {
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"insult_score": 0.006011867,
"p... | {
"num_done": {
"figure": 0,
"algorithm": 3
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} | [] | "algorithm" | "65962d6b-c013-4a90-b2ed-f36af9e242dc" | 560 | easy | |
\begin{algorithmic}
\State \textbf{Input:} Two graphs $\mathcal{G}=(V, E, \boldsymbol{X})$ and $\mathcal{H}=(P, F, \boldsymbol{Y})$
\State $c_{\boldsymbol{v}}^{(0)} \leftarrow \textsc{Hash}(\mathcal{G}[\boldsymbol{v}]), \forall \boldsymbol{v} \in V^k$
\State $d_{\boldsymbol{p}}^{(0)} \leftarrow \textsc{Hash... | \begin{algorithmic}
\State \textbf{Input:} Two graphs $\mathcal{G}=(V, E, \boldsymbol{X})$ and $\mathcal{H}=(P, F, \boldsymbol{Y})$
\State $c_{\boldsymbol{v}}^{(0)} \leftarrow \textsc{Hash}(\mathcal{G}[\boldsymbol{v}]), \forall \boldsymbol{v} \in V^k$
\State $d_{\boldsymbol{p}}^{(0)} \leftarrow \textsc{Hash}(\mathcal{H... | "https://arxiv.org/src/2206.02059" | "2206.02059.tar.gz" | "2024-01-23" | {
"title": "empowering gnns via edge-aware weisfeiler-leman algorithm",
"id": "2206.02059",
"abstract": "message passing graph neural networks (gnns) are known to have their expressiveness upper-bounded by 1-dimensional weisfeiler-leman (1-wl) algorithm. to achieve more powerful gnns, existing attempts eith... | "2024-03-15T09:04:06.314342" | {
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"insult_score": 0.0074368757,
... | {
"num_done": {
"figure": 0,
"algorithm": 3
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} | [] | "algorithm" | "8052e22a-9519-4f39-bc84-ef117dfe5879" | 1490 | hard | |
\begin{algorithmic}[1]
\State \textbf{Input:} Initial parameters $\left(\theta^P_0, \theta^Q_0,\ldots, \theta^Q_{N-1}\right)$, learning rate $\eta$; batch size $M$; number of iteration $K$.
\State \textbf{Data:} Simulated Brownian increments $\left\{ \Delta W_{t_i, k} \right\}_{0\leq i\leq N-1, 1\leq k\leq K... | \begin{algorithmic}[1]
\State \textbf{Input:} Initial parameters $\left(\theta^P_0, \theta^Q_0,\ldots, \theta^Q_{N-1}\right)$, learning rate $\eta$; batch size $M$; number of iteration $K$.
\State \textbf{Data:} Simulated Brownian increments $\left\{ \Delta W_{t_i, k} \right\}_{0\leq i\leq N-1, 1\leq k\leq K}$
\State \... | "https://arxiv.org/src/2401.17472" | "2401.17472.tar.gz" | "2024-01-30" | {
"title": "convergence of the deep bsde method for stochastic control problems formulated through the stochastic maximum principle",
"id": "2401.17472",
"abstract": "it is well-known that decision-making problems from stochastic control can be formulated by means of forward-backward stochastic differenti... | "2024-03-15T05:11:50.224129" | {
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... | {
"num_done": {
"figure": 0,
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} | [] | "algorithm" | "55a931ce-e690-4d8c-a34b-88e2f3873446" | 1394 | hard | |
\begin{algorithm}[h]
\caption{LMC method algorithm for the computation of the bid reservation price}
\begin{algorithmic}
\Require $n > 0$
\State \textbf{Step 1} : Choose $u$ and $v$ such that the hypothesis of Theorem \ref{prop_dec_gen} are satisfied
\State \textbf{Step 2} : Generate a vector $U$ of $n$ i.i.d random v... | \begin{algorithm}
[h]
\caption{LMC method algorithm for the computation of the bid reservation price}
\begin{algorithmic}
\Require $n > 0$
\State \textbf{Step 1} : Choose $u$ and $v$ such that the hypothesis of Theorem \ref{prop_dec_gen} are satisfied
\State \textbf{Step 2} : Generate a vector $U$ of $n$ i.i.d random v... | "https://arxiv.org/src/2105.08804" | "2105.08804.tar.gz" | "2024-02-20" | {
"title": "efficient approximations for utility-based pricing",
"id": "2105.08804",
"abstract": "in a context of illiquidity, the reservation price is a well-accepted alternative to the usual martingale approach which does not apply. however, this price is not available in closed form and requires numerica... | "2024-03-15T03:14:06.406557" | {
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} | [] | "algorithm" | "32b00b82-c4ea-46a2-bae9-4eebb6fb3f16" | 1161 | medium |
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