reproducibilityindex.ai

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 [1].

Journal of Machine Learning Research (JMLR) - 2024

Website:

Venue	Year	Papers	Reproducibility Score Reproducibility Score based on Gundersen et al. (2025)	Documentation Score Global mean is the average score over the seven reproducibility variables for empirical research papers.	% 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