Reproducibility Index

Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..

Journal of Machine Learning Research (JMLR) - 2024

Documentation Rate of Empirical Papers by Reproducibility Variable

Distribution of Empirical Papers by Number of Documented Variables

Website:

Venue	Year	Papers	Reproducibility Score Reproducibility Score based on Gundersen et al. (2025). See Methods for details.	Documentation Score Documentation Score is the average score over the seven reproducibility variables for empirical research papers. See Methods for details.	% Empirical Percentage of papers that are empirical research vs theoretical research.	% Industry Percentage of empirical research papers with at least one author from Industry.	Website
JMLR	2024	421	0.49	3.73	84.32%	19.44%

Search Papers

	Pseudocode	Open Source Code	Open Datasets	Dataset Splits	Hardware Specification	Software Dependencies	Experiment Setup
A Characterization of Multioutput Learnability	✅	❌	❌	❌	❌	❌	❌	1
A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent	❌	❌	❌	❌	❌	❌	✅	1
A Data-Adaptive RKHS Prior for Bayesian Learning of Kernels in Operators	❌	❌	❌	❌	❌	❌	✅	1
A Framework for Improving the Reliability of Black-box Variational Inference	✅	✅	✅	❌	❌	❌	✅	4
A General Framework for the Analysis of Kernel-based Tests	❌	❌	❌	❌	❌	❌	❌	0
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment	✅	✅	✅	❌	❌	❌	❌	3
A Multilabel Classification Framework for Approximate Nearest Neighbor Search	✅	✅	✅	✅	✅	✅	✅	7
A New, Physics-Informed Continuous-Time Reinforcement Learning Algorithm with Performance Guarantees	✅	✅	✅	✅	✅	✅	✅	7
A Note on Entrywise Consistency for Mixed-data Matrix Completion	✅	✅	✅	✅	❌	❌	✅	5
A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks	❌	✅	✅	❌	❌	❌	✅	3
A Rainbow in Deep Network Black Boxes	❌	✅	✅	✅	❌	❌	✅	4
A Random Projection Approach to Personalized Federated Learning: Enhancing Communication Efficiency, Robustness, and Fairness	✅	✅	✅	✅	✅	✅	✅	7
A Semi-parametric Estimation of Personalized Dose-response Function Using Instrumental Variables	✅	❌	✅	✅	❌	❌	✅	4
A Statistical Experimental Design Method for Constructing Deterministic Sensing Matrices for Compressed Sensing	✅	❌	❌	❌	❌	❌	✅	2
A Survey on Multi-player Bandits	✅	❌	❌	❌	❌	❌	❌	1
A Variational Approach to Bayesian Phylogenetic Inference	✅	✅	✅	❌	✅	✅	✅	6
A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression	✅	✅	✅	✅	✅	✅	✅	7
A minimax optimal approach to high-dimensional double sparse linear regression	✅	✅	✅	✅	✅	❌	✅	6
A projected semismooth Newton method for a class of nonconvex composite programs with strong prox-regularity	✅	❌	❌	❌	✅	❌	✅	3
A tensor factorization model of multilayer network interdependence	✅	✅	✅	✅	❌	❌	✅	5
AMLB: an AutoML Benchmark	❌	✅	✅	✅	✅	✅	✅	6
Accelerated Gradient Tracking over Time-varying Graphs for Decentralized Optimization	✅	❌	✅	❌	❌	❌	✅	3
Accelerating Nuclear-norm Regularized Low-rank Matrix Optimization Through Burer-Monteiro Decomposition	✅	✅	✅	✅	✅	❌	✅	6
Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees	✅	❌	✅	❌	✅	✅	✅	5
Adaptive Latent Feature Sharing for Piecewise Linear Dimensionality Reduction	✅	❌	✅	✅	❌	❌	✅	4
Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization	✅	✅	✅	❌	❌	❌	✅	4
Additive smoothing error in backward variational inference for general state-space models	❌	❌	❌	✅	✅	❌	✅	3
Adjusted Wasserstein Distributionally Robust Estimator in Statistical Learning	❌	❌	❌	❌	❌	❌	✅	1
Aequitas Flow: Streamlining Fair ML Experimentation	❌	✅	✅	❌	❌	❌	❌	2
Almost Sure Convergence Rates Analysis and Saddle Avoidance of Stochastic Gradient Methods	✅	❌	❌	❌	❌	❌	❌	1
An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization	✅	❌	❌	❌	❌	❌	❌	1
An Algorithmic Framework for the Optimization of Deep Neural Networks Architectures and Hyperparameters	❌	✅	✅	✅	✅	✅	✅	6
An Analysis of Quantile Temporal-Difference Learning	✅	❌	❌	❌	❌	❌	✅	2
An Asymptotic Study of Discriminant and Vote-Averaging Schemes for Randomly-Projected Linear Discriminants	✅	✅	✅	✅	❌	❌	✅	5
An Embedding Framework for the Design and Analysis of Consistent Polyhedral Surrogates	❌	❌	❌	❌	❌	❌	❌	0
An Entropy-Based Model for Hierarchical Learning	✅	❌	❌	❌	❌	❌	❌	1
An Inexact Projected Regularized Newton Method for Fused Zero-norms Regularization Problems	✅	✅	✅	✅	✅	❌	✅	6
An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification	✅	✅	✅	❌	❌	❌	✅	4
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC	✅	✅	✅	❌	✅	✅	✅	6
Approximate Information Tests on Statistical Submanifolds	✅	❌	❌	❌	❌	❌	✅	2
Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models	✅	✅	✅	✅	❌	❌	✅	5
Axiomatic effect propagation in structural causal models	✅	❌	✅	❌	✅	❌	✅	4
Bagging Provides Assumption-free Stability	✅	✅	❌	❌	❌	❌	✅	3
Bayesian Regression Markets	✅	✅	✅	✅	❌	❌	✅	5
Bayesian Structural Learning with Parametric Marginals for Count Data: An Application to Microbiota Systems	✅	✅	✅	❌	❌	❌	✅	4
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning	❌	✅	✅	❌	❌	❌	❌	2
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box	✅	✅	✅	❌	❌	❌	✅	4
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria	❌	❌	❌	❌	❌	❌	✅	1
Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations	✅	✅	✅	❌	✅	❌	✅	5
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds	✅	❌	❌	❌	❌	❌	✅	2
Causal Discovery with Generalized Linear Models through Peeling Algorithms	✅	✅	✅	✅	❌	❌	✅	5
Causal effects of intervening variables in settings with unmeasured confounding	✅	❌	✅	❌	❌	❌	❌	2
Causal-learn: Causal Discovery in Python	❌	✅	✅	❌	❌	❌	❌	2
Characterization of translation invariant MMD on Rd and connections with Wasserstein distances	❌	❌	❌	❌	❌	❌	✅	1
Choosing the Number of Topics in LDA Models – A Monte Carlo Comparison of Selection Criteria	✅	✅	❌	❌	❌	❌	✅	3
Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU Networks	❌	❌	❌	❌	❌	❌	❌	0
Classification with Deep Neural Networks and Logistic Loss	❌	❌	❌	❌	❌	❌	❌	0
Cluster-Adaptive Network A/B Testing: From Randomization to Estimation	✅	❌	✅	❌	❌	❌	✅	3
Commutative Scaling of Width and Depth in Deep Neural Networks	❌	❌	❌	❌	❌	❌	✅	1
Compressed and distributed least-squares regression: convergence rates with applications to federated learning	✅	✅	✅	❌	❌	❌	✅	4
Concentration and Moment Inequalities for General Functions of Independent Random Variables with Heavy Tails	❌	❌	❌	❌	❌	❌	❌	0
Conformal Inference for Online Prediction with Arbitrary Distribution Shifts	✅	✅	✅	✅	❌	❌	✅	5
Consistent Multiclass Algorithms for Complex Metrics and Constraints	✅	✅	✅	✅	❌	❌	✅	5
Contamination-source based K-sample clustering	✅	✅	✅	❌	❌	❌	✅	4
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression	✅	❌	❌	❌	❌	❌	❌	1
Continuous Prediction with Experts' Advice	❌	❌	❌	❌	❌	❌	❌	0
Convergence for nonconvex ADMM, with applications to CT imaging	✅	✅	❌	❌	❌	❌	✅	3
Convergence of Message-Passing Graph Neural Networks with Generic Aggregation on Large Random Graphs	❌	✅	❌	❌	❌	❌	✅	2
Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"	❌	❌	❌	❌	❌	❌	❌	0
Countering the Communication Bottleneck in Federated Learning: A Highly Efficient Zero-Order Optimization Technique	✅	❌	✅	❌	❌	✅	✅	4
Critically Assessing the State of the Art in Neural Network Verification	❌	❌	✅	❌	✅	✅	✅	4
DAG-Informed Structure Learning from Multi-Dimensional Point Processes	✅	❌	✅	✅	❌	❌	✅	4
Data Summarization via Bilevel Optimization	✅	❌	✅	✅	✅	❌	✅	5
Data Thinning for Convolution-Closed Distributions	✅	✅	✅	✅	❌	❌	✅	5
Data-Efficient Policy Evaluation Through Behavior Policy Search	✅	❌	✅	❌	❌	❌	✅	3
Data-driven Automated Negative Control Estimation (DANCE): Search for, Validation of, and Causal Inference with Negative Controls	✅	✅	✅	❌	❌	❌	✅	4
Debiasing Evaluations That Are Biased by Evaluations	✅	✅	✅	✅	❌	❌	✅	5
Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics	✅	✅	✅	✅	❌	✅	✅	6
Decomposing Global Feature Effects Based on Feature Interactions	✅	✅	✅	✅	❌	❌	✅	5
Decorrelated Variable Importance	❌	❌	❌	❌	❌	❌	✅	1
Deep Backward and Galerkin Methods for the Finite State Master Equation	✅	✅	❌	❌	✅	✅	✅	5
Deep Network Approximation: Beyond ReLU to Diverse Activation Functions	❌	❌	❌	❌	❌	❌	❌	0
Deep Neural Network Approximation of Invariant Functions through Dynamical Systems	❌	❌	❌	❌	❌	❌	❌	0
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces	❌	❌	❌	❌	❌	❌	❌	0
Deep Nonparametric Quantile Regression under Covariate Shift	✅	❌	❌	✅	❌	❌	✅	3
Depth Degeneracy in Neural Networks: Vanishing Angles in Fully Connected ReLU Networks on Initialization	✅	✅	✅	❌	❌	❌	✅	4
Desiderata for Representation Learning: A Causal Perspective	✅	✅	✅	✅	❌	❌	✅	5
Differentially Private Data Release for Mixed-type Data via Latent Factor Models	✅	❌	✅	✅	❌	❌	✅	4
Differentially Private Topological Data Analysis	✅	✅	✅	❌	❌	❌	✅	4
Differentially private methods for managing model uncertainty in linear regression	❌	❌	✅	❌	❌	✅	✅	3
Distributed Estimation on Semi-Supervised Generalized Linear Model	✅	❌	✅	✅	❌	❌	✅	4
Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms	✅	❌	❌	❌	❌	❌	❌	1
Distributed Kernel-Driven Data Clustering	✅	❌	✅	❌	❌	❌	✅	3
Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective	❌	❌	❌	❌	❌	❌	❌	0
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity	✅	✅	✅	❌	❌	❌	✅	4
DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models	❌	✅	❌	❌	❌	❌	❌	1
Dropout Regularization Versus l2-Penalization in the Linear Model	❌	❌	❌	❌	❌	❌	❌	0
ENNS: Variable Selection, Regression, Classification, and Deep Neural Network for High-Dimensional Data	✅	✅	✅	✅	❌	❌	✅	5
Effect-Invariant Mechanisms for Policy Generalization	✅	✅	✅	✅	❌	❌	✅	5
Efficient Active Manifold Identification via Accelerated Iteratively Reweighted Nuclear Norm Minimization	✅	✅	❌	❌	✅	❌	✅	4
Efficient Convex Algorithms for Universal Kernel Learning	✅	✅	✅	✅	✅	❌	✅	6
Efficient Modality Selection in Multimodal Learning	✅	❌	✅	✅	❌	❌	✅	4
Empirical Design in Reinforcement Learning	❌	❌	✅	❌	❌	❌	✅	2
Entropic Gromov-Wasserstein Distances: Stability and Algorithms	✅	✅	✅	❌	✅	❌	✅	5
Estimating the Minimizer and the Minimum Value of a Regression Function under Passive Design	✅	❌	❌	❌	❌	❌	❌	1
Estimating the Replication Probability of Significant Classification Benchmark Experiments	✅	✅	✅	✅	✅	✅	✅	7
Estimation of Sparse Gaussian Graphical Models with Hidden Clustering Structure	✅	❌	✅	❌	✅	✅	✅	5
Estimation of the Order of Non-Parametric Hidden Markov Models using the Singular Values of an Integral Operator	❌	✅	✅	❌	❌	❌	✅	3
Euler Characteristic Tools for Topological Data Analysis	❌	✅	✅	✅	✅	❌	✅	5
Evidence Estimation in Gaussian Graphical Models Using a Telescoping Block Decomposition of the Precision Matrix	✅	✅	✅	✅	✅	❌	✅	6
Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification	❌	❌	❌	❌	❌	❌	❌	0
Exploration of the Search Space of Gaussian Graphical Models for Paired Data	✅	✅	✅	❌	❌	❌	✅	4
Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment	✅	❌	❌	❌	❌	❌	✅	2
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition	❌	❌	❌	❌	❌	❌	❌	0
Fairness guarantees in multi-class classification with demographic parity	✅	✅	✅	✅	✅	❌	✅	6
Fairness in Survival Analysis with Distributionally Robust Optimization	✅	✅	✅	✅	✅	✅	✅	7
False discovery proportion envelopes with m-consistency	❌	✅	✅	❌	❌	❌	✅	3
Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization	✅	❌	❌	❌	❌	❌	✅	2
Fast Rates in Pool-Based Batch Active Learning	✅	❌	❌	❌	❌	❌	❌	1
Faster Randomized Methods for Orthogonality Constrained Problems	✅	❌	✅	❌	❌	❌	✅	3
Faster Rates of Differentially Private Stochastic Convex Optimization	✅	❌	✅	❌	❌	❌	✅	3
Fat-Shattering Dimension of k-fold Aggregations	❌	❌	❌	❌	❌	❌	❌	0
FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization	✅	✅	✅	✅	❌	❌	✅	5
Federated Automatic Differentiation	✅	✅	✅	✅	❌	❌	✅	5
Fermat Distances: Metric Approximation, Spectral Convergence, and Clustering Algorithms	✅	✅	❌	❌	❌	❌	✅	3
FineMorphs: Affine-Diffeomorphic Sequences for Regression	❌	✅	✅	✅	✅	✅	✅	6
Finite-time Analysis of Globally Nonstationary Multi-Armed Bandits	✅	✅	✅	❌	❌	❌	✅	4
Fisher information dissipation for time-inhomogeneous stochastic differential equations	❌	❌	❌	❌	❌	❌	✅	1
Fixed points of nonnegative neural networks	❌	✅	✅	❌	❌	❌	✅	3
Flexible Bayesian Product Mixture Models for Vector Autoregressions	❌	❌	✅	✅	✅	❌	✅	4
Fortuna: A Library for Uncertainty Quantification in Deep Learning	✅	✅	❌	❌	❌	❌	❌	2
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling	❌	❌	✅	✅	❌	❌	❌	2
From Small Scales to Large Scales: Distance-to-Measure Density based Geometric Analysis of Complex Data	❌	✅	❌	✅	❌	✅	✅	4
From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs	✅	✅	❌	❌	✅	❌	✅	4
Fréchet Random Forests for Metric Space Valued Regression with Non Euclidean Predictors	❌	✅	✅	✅	❌	❌	✅	4
Functional Directed Acyclic Graphs	✅	❌	✅	❌	✅	❌	✅	4
Functional optimal transport: regularized map estimation and domain adaptation for functional data	✅	❌	✅	❌	✅	❌	✅	4
Functions with average smoothness: structure, algorithms, and learning	❌	❌	❌	❌	❌	❌	❌	0
GGD: Grafting Gradient Descent	✅	✅	✅	✅	✅	❌	✅	6
Gaussian Interpolation Flows	❌	❌	✅	❌	❌	❌	❌	1
Gaussian Mixture Models with Rare Events	❌	❌	✅	✅	❌	❌	✅	3
Generalization and Stability of Interpolating Neural Networks with Minimal Width	❌	❌	❌	❌	❌	❌	❌	0
Generalization on the Unseen, Logic Reasoning and Degree Curriculum	✅	✅	❌	✅	✅	❌	✅	5
Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables	✅	❌	✅	❌	❌	❌	✅	3
Generative Adversarial Ranking Nets	✅	✅	✅	✅	❌	❌	✅	5
Geometric Learning with Positively Decomposable Kernels	❌	❌	❌	❌	❌	❌	❌	0
Goal-Space Planning with Subgoal Models	✅	❌	✅	❌	❌	❌	✅	3
Gradient-free optimization of highly smooth functions: improved analysis and a new algorithm	❌	❌	❌	❌	❌	❌	❌	0
Gradual Domain Adaptation: Theory and Algorithms	✅	✅	✅	✅	✅	✅	✅	7
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length	✅	✅	✅	❌	✅	❌	✅	5
Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data	❌	✅	❌	❌	❌	❌	✅	2
Grokking phase transitions in learning local rules with gradient descent	❌	❌	❌	❌	❌	❌	✅	1
Guaranteed Nonconvex Factorization Approach for Tensor Train Recovery	❌	❌	❌	❌	❌	❌	✅	1
Hamiltonian Monte Carlo for efficient Gaussian sampling: long and random steps	✅	❌	❌	❌	❌	❌	❌	1
Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis	✅	❌	❌	✅	❌	❌	✅	3
Heterogeneous-Agent Reinforcement Learning	✅	✅	✅	❌	✅	❌	✅	5
High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise	✅	✅	❌	❌	❌	❌	✅	3
High Probability and Risk-Averse Guarantees for a Stochastic Accelerated Primal-Dual Method	✅	❌	✅	❌	❌	❌	✅	3
Homeomorphic Projection to Ensure Neural-Network Solution Feasibility for Constrained Optimization	✅	✅	✅	✅	✅	❌	✅	6
How Two-Layer Neural Networks Learn, One (Giant) Step at a Time	✅	✅	❌	❌	❌	❌	✅	3
Identifiability and Asymptotics in Learning Homogeneous Linear ODE Systems from Discrete Observations	❌	❌	❌	❌	❌	❌	✅	1
Identifying Causal Eﬀects using Instrumental Time Series: Nuisance IV and Correcting for the Past	✅	✅	❌	❌	❌	❌	✅	3
Improved Random Features for Dot Product Kernels	✅	✅	✅	✅	✅	✅	✅	7
Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions	❌	✅	✅	✅	❌	❌	✅	4
Improving physics-informed neural networks with meta-learned optimization	❌	✅	❌	❌	❌	✅	✅	3
Individual-centered Partial Information in Social Networks	✅	❌	✅	❌	❌	❌	✅	3
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints	❌	✅	✅	❌	❌	❌	✅	3
Inference on High-dimensional Single-index Models with Streaming Data	✅	❌	✅	✅	❌	❌	✅	4
Infinite-Dimensional Diffusion Models	✅	❌	❌	❌	❌	❌	✅	2
Information Capacity Regret Bounds for Bandits with Mediator Feedback	✅	❌	❌	❌	❌	❌	❌	1
Information Processing Equalities and the Information–Risk Bridge	❌	❌	❌	❌	❌	❌	❌	0
Information-Theoretic Generalization Bounds for Transductive Learning and its Applications	❌	❌	✅	✅	❌	❌	✅	3
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Interpretable algorithmic fairness in structured and unstructured data	✅	✅	✅	✅	❌	❌	✅	5
Invariant Physics-Informed Neural Networks for Ordinary Differential Equations	❌	❌	❌	❌	❌	❌	✅	1
Invariant and Equivariant Reynolds Networks	✅	✅	✅	✅	✅	❌	✅	6
Iterate Averaging in the Quest for Best Test Error	✅	❌	✅	❌	✅	✅	✅	5
Just Wing It: Near-Optimal Estimation of Missing Mass in a Markovian Sequence	✅	✅	✅	✅	❌	❌	✅	5
KerasCV and KerasNLP: Multi-framework Models	❌	✅	✅	❌	✅	❌	✅	4
Kernel Thinning	✅	✅	✅	❌	❌	❌	✅	4
Label Alignment Regularization for Distribution Shift	✅	✅	✅	✅	❌	❌	✅	5
Label Noise Robustness of Conformal Prediction	✅	✅	✅	✅	❌	❌	✅	5
Law of Large Numbers and Central Limit Theorem for Wide Two-layer Neural Networks: The Mini-Batch and Noisy Case	❌	❌	✅	❌	❌	❌	✅	2
Learnability of Linear Port-Hamiltonian Systems	❌	✅	❌	✅	❌	❌	✅	3
Learning Discretized Neural Networks under Ricci Flow	✅	❌	✅	✅	✅	❌	✅	5
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach	✅	❌	❌	❌	❌	❌	❌	1
Learning Gaussian DAGs from Network Data	✅	❌	✅	❌	❌	❌	✅	3
Learning Non-Gaussian Graphical Models via Hessian Scores and Triangular Transport	✅	✅	✅	❌	❌	❌	✅	4
Learning Optimal Dynamic Treatment Regimens Subject to Stagewise Risk Controls	✅	❌	❌	✅	❌	❌	✅	3
Learning Regularized Graphon Mean-Field Games with Unknown Graphons	✅	❌	❌	❌	✅	❌	✅	3
Learning and scoring Gaussian latent variable causal models with unknown additive interventions	✅	✅	✅	✅	❌	❌	✅	5
Learning from many trajectories	❌	❌	❌	❌	❌	❌	✅	1
Learning to Warm-Start Fixed-Point Optimization Algorithms	❌	✅	✅	✅	❌	❌	✅	4
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks	❌	❌	✅	✅	❌	❌	✅	3
Learning with a linear loss function: excess risk and estimation bounds for ERM, minmax MOM and their regularized versions with applications to robustness in sparse PCA.	✅	❌	❌	❌	❌	❌	❌	1
Line Graph Vietoris-Rips Persistence Diagram for Topological Graph Representation Learning	✅	✅	✅	✅	✅	❌	✅	6
Linear Distance Metric Learning with Noisy Labels	❌	✅	✅	✅	❌	❌	✅	4
Linear Regression With Unmatched Data: A Deconvolution Perspective	❌	❌	✅	❌	❌	❌	✅	2
Localisation of Regularised and Multiview Support Vector Machine Learning	✅	❌	❌	❌	❌	❌	✅	2
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond	❌	✅	✅	✅	❌	❌	✅	4
Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning	✅	✅	✅	❌	✅	❌	✅	5
Logistic Regression Under Network Dependence	✅	❌	✅	❌	❌	❌	✅	3
Low-Rank Matrix Estimation in the Presence of Change-Points	✅	✅	✅	✅	❌	❌	✅	5
Low-rank Variational Bayes correction to the Laplace method	✅	✅	✅	❌	❌	❌	✅	4
Lower Bounds on the Bayesian Risk via Information Measures	❌	❌	❌	❌	❌	❌	❌	0
Lower Complexity Adaptation for Empirical Entropic Optimal Transport	❌	✅	❌	❌	❌	❌	✅	2
Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction	✅	❌	❌	❌	❌	❌	❌	1
MAP- and MLE-Based Teaching	❌	❌	❌	❌	❌	❌	❌	0
MLRegTest: A Benchmark for the Machine Learning of Regular Languages	❌	✅	✅	✅	❌	❌	✅	4
Manifold Learning by Mixture Models of VAEs for Inverse Problems	✅	✅	❌	❌	❌	❌	✅	3
Margin-Based Active Learning of Classifiers	✅	❌	❌	❌	❌	❌	❌	1
Materials Discovery using Max K-Armed Bandit	✅	❌	❌	❌	❌	❌	✅	2
Mathematical Framework for Online Social Media Auditing	✅	❌	❌	❌	❌	❌	❌	1
Matryoshka Policy Gradient for Entropy-Regularized RL: Convergence and Global Optimality	✅	❌	✅	❌	❌	❌	✅	3
Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)	✅	❌	❌	❌	✅	❌	✅	3
Mean-Field Games With Finitely Many Players: Independent Learning and Subjectivity	✅	❌	❌	❌	❌	❌	✅	2
Mean-Square Analysis of Discretized Itô Diffusions for Heavy-tailed Sampling	❌	❌	❌	❌	❌	❌	✅	1
Measuring Sample Quality in Algorithms for Intractable Normalizing Function Problems	❌	✅	✅	❌	✅	❌	✅	4
Memorization With Neural Nets: Going Beyond the Worst Case	✅	✅	✅	❌	❌	❌	❌	3
Memory of recurrent networks: Do we compute it right?	✅	✅	❌	❌	❌	❌	✅	3
Memory-Efficient Sequential Pattern Mining with Hybrid Tries	✅	✅	✅	❌	✅	❌	✅	5
Mentored Learning: Improving Generalization and Convergence of Student Learner	✅	❌	✅	✅	❌	❌	✅	4
Minimax Rates for High-Dimensional Random Tessellation Forests	❌	❌	❌	❌	❌	❌	❌	0
Model-Free Representation Learning and Exploration in Low-Rank MDPs	✅	❌	❌	❌	❌	❌	❌	1
Modeling Random Networks with Heterogeneous Reciprocity	✅	❌	✅	❌	❌	❌	✅	3
Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization	❌	✅	✅	✅	✅	❌	✅	5
More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization	❌	✅	✅	✅	❌	❌	✅	4
More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity	❌	❌	✅	❌	✅	❌	✅	3
Multi-Objective Neural Architecture Search by Learning Search Space Partitions	✅	❌	✅	✅	✅	❌	✅	5
Multi-Response Linear Discriminant Analysis in High Dimensions	✅	❌	✅	✅	❌	❌	✅	4
Multi-class Probabilistic Bounds for Majority Vote Classifiers with Partially Labeled Data	✅	✅	✅	✅	✅	❌	✅	6
Multiple Descent in the Multiple Random Feature Model	❌	✅	❌	✅	❌	❌	✅	3
Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization	✅	✅	❌	❌	❌	❌	✅	3
Nearest Neighbor Sampling for Covariate Shift Adaptation	✅	❌	✅	✅	❌	❌	✅	4
Neural Bayes estimators for censored inference with peaks-over-threshold models	❌	✅	✅	❌	❌	✅	✅	4
Neural Collapse for Unconstrained Feature Model under Cross-entropy Loss with Imbalanced Data	❌	✅	✅	✅	❌	❌	✅	4
Neural Feature Learning in Function Space	❌	✅	❌	✅	❌	❌	✅	3
Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space	❌	❌	❌	❌	❌	❌	✅	1
Neural Networks with Sparse Activation Induced by Large Bias: Tighter Analysis with Bias-Generalized NTK	❌	❌	✅	❌	❌	❌	✅	2
Non-Euclidean Monotone Operator Theory and Applications	✅	✅	❌	❌	❌	❌	✅	3
Non-splitting Neyman-Pearson Classifiers	❌	❌	✅	✅	❌	❌	✅	3
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization	❌	❌	❌	❌	❌	❌	❌	0
Nonparametric Copula Models for Multivariate, Mixed, and Missing Data	✅	✅	✅	❌	✅	❌	✅	5
Nonparametric Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks	✅	❌	✅	✅	❌	❌	✅	4
Nonparametric Inference under B-bits Quantization	✅	❌	✅	❌	❌	❌	✅	3
Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks	❌	❌	❌	❌	❌	❌	❌	0
Nonparametric Regression for 3D Point Cloud Learning	✅	❌	✅	❌	✅	❌	✅	4
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees	✅	✅	✅	✅	✅	❌	✅	6
Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning	✅	✅	✅	✅	❌	❌	✅	5
OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research	❌	✅	✅	❌	❌	❌	❌	2
On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis for Parametric Models	✅	❌	✅	✅	❌	❌	✅	4
On Doubly Robust Inference for Double Machine Learning in Semiparametric Regression	✅	❌	❌	✅	❌	❌	✅	3
On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models	✅	✅	✅	❌	✅	✅	✅	6
On Regularized Radon-Nikodym Differentiation	✅	❌	❌	❌	❌	❌	✅	2
On Sufficient Graphical Models	✅	✅	✅	❌	❌	❌	✅	4
On Tail Decay Rate Estimation of Loss Function Distributions	✅	✅	✅	✅	❌	❌	✅	5
On Truthing Issues in Supervised Classification	✅	❌	✅	✅	❌	❌	✅	4
On Unbiased Estimation for Partially Observed Diffusions	✅	✅	✅	❌	❌	❌	✅	4
On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling	✅	❌	❌	❌	❌	❌	✅	2
On the Computational and Statistical Complexity of Over-parameterized Matrix Sensing	❌	❌	❌	❌	❌	❌	✅	1
On the Concentration of the Minimizers of Empirical Risks	❌	❌	❌	❌	❌	❌	❌	0
On the Connection between Lp- and Risk Consistency and its Implications on Regularized Kernel Methods	❌	❌	❌	❌	❌	❌	❌	0
On the Convergence of Projected Alternating Maximization for Equitable and Optimal Transport	✅	❌	❌	❌	❌	❌	✅	2
On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks	❌	❌	❌	❌	❌	❌	✅	1
On the Eigenvalue Decay Rates of a Class of Neural-Network Related Kernel Functions Defined on General Domains	❌	❌	❌	❌	❌	❌	✅	1
On the Generalization of Stochastic Gradient Descent with Momentum	❌	❌	✅	✅	✅	❌	✅	4
On the Hyperparameters in Stochastic Gradient Descent with Momentum	❌	❌	✅	❌	❌	❌	✅	2
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training	✅	✅	✅	❌	✅	❌	✅	5
On the Intrinsic Structures of Spiking Neural Networks	✅	❌	✅	✅	❌	❌	✅	4
On the Learnability of Out-of-distribution Detection	❌	❌	❌	❌	❌	❌	❌	0
On the Optimality of Gaussian Kernel Based Nonparametric Tests against Smooth Alternatives	❌	❌	✅	❌	❌	❌	✅	2
On the Optimality of Misspecified Spectral Algorithms	❌	❌	❌	❌	❌	❌	✅	1
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control	✅	❌	❌	❌	❌	❌	✅	2
Open-Source Conversational AI with SpeechBrain 1.0	❌	✅	✅	❌	✅	✅	❌	4
OpenBox: A Python Toolkit for Generalized Black-box Optimization	❌	✅	✅	❌	❌	❌	❌	2
Operator learning without the adjoint	✅	✅	✅	❌	❌	❌	❌	3
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions	✅	❌	✅	✅	❌	❌	✅	4
Optimal Bump Functions for Shallow ReLU networks: Weight Decay, Depth Separation, Curse of Dimensionality	❌	✅	❌	❌	❌	❌	✅	2
Optimal Clustering with Bandit Feedback	✅	❌	✅	❌	❌	❌	✅	3
Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes	✅	✅	✅	❌	❌	❌	❌	3
Optimal First-Order Algorithms as a Function of Inequalities	✅	✅	❌	❌	❌	❌	❌	2
Optimal Learning Policies for Differential Privacy in Multi-armed Bandits	✅	❌	❌	❌	❌	❌	✅	2
Optimal Locally Private Nonparametric Classification with Public Data	✅	✅	✅	❌	✅	❌	✅	5
Optimal Scaling for the Proximal Langevin Algorithm in High Dimensions	❌	❌	❌	❌	❌	❌	❌	0
Optimal Weighted Random Forests	✅	✅	✅	✅	❌	❌	✅	5
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization	✅	❌	❌	❌	❌	❌	❌	1
Optimistic Search: Change Point Estimation for Large-scale Data via Adaptive Logarithmic Queries	✅	❌	✅	❌	❌	✅	✅	4
Optimization-based Causal Estimation from Heterogeneous Environments	✅	✅	✅	✅	❌	❌	✅	5
Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance	✅	✅	❌	❌	❌	❌	❌	2
Overparametrized Multi-layer Neural Networks: Uniform Concentration of Neural Tangent Kernel and Convergence of Stochastic Gradient Descent	❌	❌	✅	❌	❌	❌	✅	2
PAMI: An Open-Source Python Library for Pattern Mining	❌	✅	✅	❌	❌	❌	❌	2
PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium	✅	❌	❌	❌	❌	❌	✅	2
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX	❌	✅	❌	❌	✅	✅	✅	4
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates	✅	✅	✅	❌	❌	❌	✅	4
Parallel-in-Time Probabilistic Numerical ODE Solvers	✅	✅	✅	❌	✅	❌	✅	5
Pareto Smoothed Importance Sampling	✅	❌	✅	✅	✅	✅	✅	6
Pearl: A Production-Ready Reinforcement Learning Agent	❌	✅	✅	❌	✅	❌	✅	4
Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling	❌	❌	✅	❌	❌	❌	✅	2
Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport	✅	❌	❌	❌	❌	✅	✅	3
Personalized PCA: Decoupling Shared and Unique Features	✅	✅	✅	✅	❌	❌	✅	5
PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design	❌	✅	✅	✅	✅	❌	✅	5
PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks	❌	✅	✅	❌	❌	❌	✅	3
Policy Gradient Methods in the Presence of Symmetries and State Abstractions	✅	✅	✅	❌	✅	✅	✅	6
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks	❌	❌	✅	❌	❌	❌	✅	2
Post-Regularization Confidence Bands for Ordinary Differential Equations	✅	❌	✅	✅	❌	❌	✅	4
Power of knockoff: The impact of ranking algorithm, augmented design, and symmetric statistic	❌	❌	❌	❌	❌	❌	✅	1
Pre-trained Gaussian Processes for Bayesian Optimization	✅	✅	✅	✅	✅	✅	✅	7
Predictive Inference with Weak Supervision	✅	❌	✅	✅	❌	❌	✅	4
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization	✅	✅	✅	✅	✅	❌	✅	6
PromptBench: A Unified Library for Evaluation of Large Language Models	❌	✅	✅	✅	❌	❌	❌	3
Pure Differential Privacy for Functional Summaries with a Laplace-like Process	✅	❌	✅	❌	❌	❌	✅	3
Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension	✅	❌	✅	❌	✅	✅	✅	5
PyDMD: A Python Package for Robust Dynamic Mode Decomposition	✅	✅	✅	❌	❌	❌	✅	4
PyGOD: A Python Library for Graph Outlier Detection	❌	✅	✅	❌	❌	❌	✅	3
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization	❌	✅	✅	❌	❌	❌	✅	3
Pygmtools: A Python Graph Matching Toolkit	❌	✅	❌	❌	❌	❌	❌	1
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration	✅	✅	✅	❌	✅	❌	❌	4
RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control	✅	✅	✅	❌	✅	❌	✅	5
Random Forest Weighted Local Fréchet Regression with Random Objects	✅	❌	✅	✅	❌	❌	✅	4
Random Fully Connected Neural Networks as Perturbatively Solvable Hierarchies	❌	❌	❌	❌	❌	❌	❌	0
Random Smoothing Regularization in Kernel Gradient Descent Learning	❌	❌	✅	✅	❌	❌	✅	3
Random Subgraph Detection Using Queries	✅	❌	❌	❌	❌	❌	❌	1
Random measure priors in Bayesian recovery from sketches	❌	✅	✅	❌	❌	❌	✅	3
Rates of convergence for density estimation with generative adversarial networks	❌	❌	❌	❌	❌	❌	❌	0
Recursive Estimation of Conditional Kernel Mean Embeddings	✅	❌	❌	❌	❌	❌	❌	1
Regimes of No Gain in Multi-class Active Learning	✅	❌	❌	✅	❌	❌	✅	3
Regret Analysis of Bilateral Trade with a Smoothed Adversary	✅	❌	❌	❌	❌	❌	❌	1
Representation Learning via Manifold Flattening and Reconstruction	✅	✅	✅	❌	❌	❌	✅	4
Resource-Efficient Neural Networks for Embedded Systems	❌	❌	✅	✅	✅	❌	✅	4
Rethinking Discount Regularization: New Interpretations, Unintended Consequences, and Solutions for Regularization in Reinforcement Learning	✅	✅	❌	❌	❌	❌	❌	2
Revisiting RIP Guarantees for Sketching Operators on Mixture Models	❌	❌	❌	❌	❌	❌	❌	0
Risk Measures and Upper Probabilities: Coherence and Stratification	❌	❌	✅	✅	❌	❌	✅	3
Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Robust Principal Component Analysis using Density Power Divergence	❌	❌	✅	❌	❌	❌	✅	2
Robust Spectral Clustering with Rank Statistics	✅	✅	✅	❌	❌	❌	✅	4
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds	✅	✅	✅	❌	❌	❌	✅	4
Sample Complexity of Variance-Reduced Distributionally Robust Q-Learning	✅	❌	❌	❌	❌	❌	✅	2
Sample-efficient Adversarial Imitation Learning	✅	❌	✅	✅	✅	❌	✅	5
Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data	✅	❌	✅	✅	✅	❌	✅	5
Scalable Resampling in Massive Generalized Linear Models via Subsampled Residual Bootstrap	✅	❌	✅	❌	❌	❌	✅	3
Scaled Conjugate Gradient Method for Nonconvex Optimization in Deep Neural Networks	✅	✅	✅	✅	✅	✅	✅	7
Scaling Instruction-Finetuned Language Models	❌	✅	✅	✅	✅	❌	✅	5
Scaling Speech Technology to 1,000+ Languages	✅	✅	✅	✅	✅	❌	✅	6
Scaling the Convex Barrier with Sparse Dual Algorithms	✅	✅	✅	✅	✅	❌	✅	6
Seeded Graph Matching for the Correlated Gaussian Wigner Model via the Projected Power Method	✅	✅	✅	❌	❌	✅	✅	5
Semi-supervised Inference for Block-wise Missing Data without Imputation	✅	❌	✅	✅	❌	❌	✅	4
Sharp analysis of power iteration for tensor PCA	❌	❌	❌	❌	❌	❌	✅	1
Sharpness-Aware Minimization and the Edge of Stability	❌	✅	✅	✅	✅	✅	✅	6
Simple Cycle Reservoirs are Universal	❌	❌	❌	❌	❌	❌	❌	0
Sparse Graphical Linear Dynamical Systems	✅	✅	✅	❌	✅	✅	✅	6
Sparse NMF with Archetypal Regularization: Computational and Robustness Properties	✅	✅	✅	❌	✅	❌	✅	5
Sparse Recovery With Multiple Data Streams: An Adaptive Sequential Testing Approach	✅	❌	✅	❌	❌	❌	✅	3
Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces	❌	❌	❌	❌	❌	❌	❌	0
Spatial meshing for general Bayesian multivariate models	✅	✅	✅	✅	✅	✅	✅	7
Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks	❌	❌	✅	✅	❌	❌	✅	3
Spectral Regularized Kernel Goodness-of-Fit Tests	❌	❌	❌	❌	❌	❌	✅	1
Spectral learning of multivariate extremes	✅	❌	✅	✅	❌	❌	✅	4
Spherical Rotation Dimension Reduction with Geometric Loss Functions	✅	✅	✅	❌	❌	❌	✅	4
Split Conformal Prediction and Non-Exchangeable Data	❌	✅	✅	✅	✅	❌	✅	5
Stability and L2-penalty in Model Averaging	✅	❌	✅	✅	❌	❌	✅	4
Stable Implementation of Probabilistic ODE Solvers	✅	✅	✅	❌	❌	❌	✅	4
Stable and Consistent Density-Based Clustering via Multiparameter Persistence	✅	✅	✅	❌	✅	✅	✅	6
Stage-Aware Learning for Dynamic Treatments	✅	✅	✅	✅	❌	❌	✅	5
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case	❌	✅	❌	❌	❌	❌	✅	2
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces	✅	✅	❌	❌	❌	❌	❌	2
Statistical Inference for Fairness Auditing	✅	✅	✅	✅	✅	❌	✅	6
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression	❌	❌	❌	❌	❌	❌	❌	0
Statistical analysis for a penalized EM algorithm in high-dimensional mixture linear regression model	✅	❌	✅	✅	❌	❌	✅	4
Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality	✅	✅	❌	❌	❌	❌	✅	3
Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent	❌	❌	❌	❌	❌	❌	❌	0
Stochastic Regularized Majorization-Minimization with weakly convex and multi-convex surrogates	✅	✅	✅	❌	❌	❌	✅	4
Stochastic-Constrained Stochastic Optimization with Markovian Data	✅	❌	❌	❌	❌	❌	✅	2
Structured Dynamic Pricing: Optimal Regret in a Global Shrinkage Model	✅	❌	✅	❌	❌	❌	✅	3
Structured Optimal Variational Inference for Dynamic Latent Space Models	❌	✅	✅	✅	❌	✅	✅	5
Studying the Interplay between Information Loss and Operation Loss in Representations for Classification	❌	❌	❌	❌	❌	❌	✅	1
Sum-of-norms clustering does not separate nearby balls	❌	✅	❌	❌	❌	❌	✅	2
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee	❌	✅	✅	✅	✅	❌	✅	5
Tangential Wasserstein Projections	❌	✅	✅	❌	✅	❌	❌	3
Targeted Separation and Convergence with Kernel Discrepancies	❌	❌	❌	❌	❌	❌	❌	0
Tensor-train methods for sequential state and parameter learning in state-space models	✅	✅	✅	❌	✅	❌	✅	5
The Loss Landscape of Deep Linear Neural Networks: a Second-order Analysis	❌	❌	❌	❌	❌	❌	✅	1
The Non-Overlapping Statistical Approximation to Overlapping Group Lasso	✅	❌	✅	✅	❌	✅	✅	5
The Nyström method for convex loss functions	❌	❌	✅	✅	✅	❌	✅	4
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective	✅	✅	❌	❌	❌	❌	✅	3
Three-Way Trade-Off in Multi-Objective Learning: Optimization, Generalization and Conflict-Avoidance	✅	✅	✅	✅	✅	✅	✅	7
Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions	❌	❌	✅	✅	❌	✅	✅	4
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains	❌	✅	✅	❌	❌	❌	❌	2
Topological Analysis for Detecting Anomalies in dependent sequences: application to Time Series	✅	✅	✅	✅	✅	❌	✅	6
Topological Node2vec: Enhanced Graph Embedding via Persistent Homology	✅	✅	❌	❌	❌	❌	✅	3
Towards Explainable Evaluation Metrics for Machine Translation	❌	❌	❌	❌	❌	❌	❌	0
Towards Optimal Sobolev Norm Rates for the Vector-Valued Regularized Least-Squares Algorithm	❌	❌	❌	❌	❌	❌	❌	0
Towards Unbiased Exploration in Partial Label Learning	❌	✅	✅	✅	✅	❌	✅	5
Trained Transformers Learn Linear Models In-Context	❌	❌	❌	❌	❌	❌	✅	1
Training Integrable Parameterizations of Deep Neural Networks in the Infinite-Width Limit	❌	✅	✅	✅	❌	❌	✅	4
Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST)	✅	❌	✅	✅	❌	❌	✅	4
Transfer learning for tensor Gaussian graphical models	✅	❌	✅	✅	❌	❌	✅	4
Transport-based Counterfactual Models	❌	✅	✅	✅	❌	❌	✅	4
Triple Component Matrix Factorization: Untangling Global, Local, and Noisy Components	✅	✅	✅	✅	✅	❌	✅	6
Two is Better Than One: Regularized Shrinkage of Large Minimum Variance Portfolios	❌	❌	✅	✅	❌	✅	✅	4
Uncertainty Quantification of MLE for Entity Ranking with Covariates	✅	❌	✅	✅	❌	❌	✅	4
Understanding Entropic Regularization in GANs	✅	❌	❌	❌	❌	❌	✅	2
Unified Binary and Multiclass Margin-Based Classification	❌	❌	❌	❌	❌	❌	❌	0
Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets	❌	❌	❌	❌	❌	❌	❌	0
Unlabeled Principal Component Analysis and Matrix Completion	✅	❌	✅	❌	✅	❌	✅	4
Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need?	❌	✅	✅	❌	❌	❌	✅	3
Unsupervised Tree Boosting for Learning Probability Distributions	✅	✅	✅	✅	✅	❌	✅	6
Value-Distributional Model-Based Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Variance estimation in graphs with the fused lasso	✅	❌	✅	❌	❌	❌	✅	3
Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression	❌	✅	✅	✅	❌	❌	✅	4
Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks	✅	❌	✅	❌	✅	❌	✅	4
Virtual-Event-Based Posterior Sampling and Inference for Neyman-Scott Processes	✅	❌	✅	❌	✅	❌	✅	4
Volterra Neural Networks (VNNs)	❌	✅	✅	✅	❌	❌	❌	3
Wasserstein Proximal Coordinate Gradient Algorithms	✅	❌	✅	✅	❌	❌	✅	4
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?	✅	✅	✅	✅	✅	❌	✅	6
Win: Weight-Decay-Integrated Nesterov Acceleration for Faster Network Training	✅	✅	✅	✅	✅	❌	✅	6
Zeroth-order Stochastic Approximation Algorithms for DR-submodular Optimization	✅	❌	✅	❌	❌	✅	✅	4
aeon: a Python Toolkit for Learning from Time Series	❌	✅	✅	✅	❌	❌	❌	3
pgmpy: A Python Toolkit for Bayesian Networks	❌	✅	❌	❌	❌	❌	❌	1
ptwt - The PyTorch Wavelet Toolbox	❌	✅	❌	❌	✅	❌	✅	3
skscope: Fast Sparsity-Constrained Optimization in Python	❌	✅	❌	❌	✅	✅	✅	4