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) - 2022

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	2022	351	0.52	3.89	84.05%	21.02%

Search Papers

	Pseudocode	Open Source Code	Open Datasets	Dataset Splits	Hardware Specification	Software Dependencies	Experiment Setup
(f,Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics	❌	❌	✅	✅	❌	❌	✅	3
A Bregman Learning Framework for Sparse Neural Networks	✅	✅	✅	✅	❌	❌	✅	5
A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One	✅	❌	✅	✅	❌	❌	✅	4
A Closer Look at Embedding Propagation for Manifold Smoothing	❌	❌	✅	✅	❌	❌	✅	3
A Computationally Efficient Framework for Vector Representation of Persistence Diagrams	❌	✅	✅	✅	❌	✅	✅	5
A Distribution Free Conditional Independence Test with Applications to Causal Discovery	✅	❌	✅	❌	❌	❌	✅	3
A Forward Approach for Sufficient Dimension Reduction in Binary Classification	✅	❌	✅	✅	❌	❌	✅	4
A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning	❌	✅	✅	❌	❌	❌	✅	3
A Kernel Two-Sample Test for Functional Data	❌	✅	✅	✅	❌	❌	✅	4
A Momentumized, Adaptive, Dual Averaged Gradient Method	✅	✅	✅	✅	✅	❌	✅	6
A Nonconvex Framework for Structured Dynamic Covariance Recovery	✅	✅	✅	✅	❌	❌	✅	5
A Perturbation-Based Kernel Approximation Framework	❌	✅	✅	✅	❌	❌	✅	4
A Primer for Neural Arithmetic Logic Modules	❌	✅	✅	✅	❌	❌	✅	4
A Random Matrix Perspective on Random Tensors	❌	❌	❌	❌	❌	❌	❌	0
A Statistical Approach for Optimal Topic Model Identification	❌	✅	✅	❌	❌	✅	❌	3
A Stochastic Bundle Method for Interpolation	✅	✅	✅	✅	✅	❌	✅	6
A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review	❌	✅	✅	❌	❌	✅	✅	4
A Unifying Framework for Variance-Reduced Algorithms for Findings Zeroes of Monotone operators	✅	✅	✅	❌	❌	❌	✅	4
A Wasserstein Distance Approach for Concentration of Empirical Risk Estimates	✅	❌	❌	❌	❌	❌	❌	1
A Worst Case Analysis of Calibrated Label Ranking Multi-label Classification Method	✅	❌	❌	❌	❌	❌	❌	1
A proof of convergence for the gradient descent optimization method with random initializations in the training of neural networks with ReLU activation for piecewise linear target functions	❌	❌	❌	❌	❌	❌	❌	0
A spectral-based analysis of the separation between two-layer neural networks and linear methods	❌	❌	❌	❌	❌	❌	❌	0
A universally consistent learning rule with a universally monotone error	✅	❌	❌	❌	❌	❌	❌	1
ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network	✅	❌	✅	❌	❌	❌	✅	3
Accelerated Zeroth-Order and First-Order Momentum Methods from Mini to Minimax Optimization	✅	❌	✅	❌	❌	❌	✅	3
Accelerating Adaptive Cubic Regularization of Newton's Method via Random Sampling	✅	❌	✅	✅	✅	❌	✅	5
Active Learning for Nonlinear System Identification with Guarantees	✅	❌	❌	❌	❌	❌	❌	1
Active Structure Learning of Bayesian Networks in an Observational Setting	✅	✅	✅	❌	❌	❌	✅	4
Adaptive Greedy Algorithm for Moderately Large Dimensions in Kernel Conditional Density Estimation	✅	❌	❌	❌	✅	❌	✅	3
Additive Nonlinear Quantile Regression in Ultra-high Dimension	✅	✅	✅	✅	❌	✅	✅	6
Advantage of Deep Neural Networks for Estimating Functions with Singularity on Hypersurfaces	❌	❌	❌	❌	❌	❌	❌	0
Adversarial Classification: Necessary Conditions and Geometric Flows	❌	❌	❌	❌	❌	❌	❌	0
Adversarial Robustness Guarantees for Gaussian Processes	✅	✅	✅	✅	❌	❌	✅	5
All You Need is a Good Functional Prior for Bayesian Deep Learning	✅	❌	✅	✅	❌	❌	✅	4
An Efficient Sampling Algorithm for Non-smooth Composite Potentials	✅	❌	❌	❌	❌	❌	✅	2
An Error Analysis of Generative Adversarial Networks for Learning Distributions	❌	❌	❌	❌	❌	❌	❌	0
An Improper Estimator with Optimal Excess Risk in Misspecified Density Estimation and Logistic Regression	❌	❌	❌	❌	❌	❌	❌	0
An Optimization-centric View on Bayes' Rule: Reviewing and Generalizing Variational Inference	✅	✅	✅	✅	❌	❌	✅	5
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks	✅	❌	✅	✅	❌	❌	✅	4
Approximate Bayesian Computation via Classification	✅	❌	✅	❌	❌	❌	✅	3
Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems	✅	✅	✅	❌	❌	❌	✅	4
Approximation and Optimization Theory for Linear Continuous-Time Recurrent Neural Networks	❌	❌	❌	❌	❌	❌	✅	1
Are All Layers Created Equal?	❌	❌	✅	✅	❌	❌	✅	3
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms	✅	❌	✅	❌	❌	❌	✅	3
Asymptotic Network Independence and Step-Size for a Distributed Subgradient Method	❌	✅	❌	❌	❌	❌	✅	2
Asymptotic Study of Stochastic Adaptive Algorithms in Non-convex Landscape	❌	❌	❌	❌	❌	❌	❌	0
Attraction-Repulsion Spectrum in Neighbor Embeddings	❌	✅	✅	✅	❌	✅	✅	5
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning	✅	✅	✅	✅	✅	✅	✅	7
Batch Normalization Preconditioning for Neural Network Training	✅	❌	✅	✅	✅	✅	✅	6
Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure	✅	❌	✅	❌	❌	❌	✅	3
Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes	✅	✅	✅	❌	❌	✅	✅	5
Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy	✅	❌	✅	❌	❌	❌	✅	3
Bayesian subset selection and variable importance for interpretable prediction and classification	✅	✅	❌	✅	❌	❌	✅	4
Behavior Priors for Efficient Reinforcement Learning	✅	✅	✅	❌	❌	❌	✅	4
Beyond Sub-Gaussian Noises: Sharp Concentration Analysis for Stochastic Gradient Descent	❌	❌	❌	❌	❌	❌	✅	1
Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution	✅	✅	✅	✅	❌	❌	✅	5
Bounding the Error of Discretized Langevin Algorithms for Non-Strongly Log-Concave Targets	❌	❌	❌	❌	❌	❌	❌	0
CD-split and HPD-split: Efficient Conformal Regions in High Dimensions	✅	✅	✅	✅	❌	❌	✅	5
Cascaded Diffusion Models for High Fidelity Image Generation	✅	❌	✅	✅	✅	❌	✅	5
Cauchy–Schwarz Regularized Autoencoder	❌	❌	✅	✅	❌	❌	✅	3
Causal Aggregation: Estimation and Inference of Causal Effects by Constraint-Based Data Fusion	✅	❌	✅	❌	❌	❌	✅	3
Causal Classification: Treatment Effect Estimation vs. Outcome Prediction	❌	✅	✅	✅	❌	❌	❌	3
Change point localization in dependent dynamic nonparametric random dot product graphs	✅	✅	✅	❌	❌	❌	✅	4
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms	❌	✅	✅	❌	✅	❌	✅	4
Clustering with Semidefinite Programming and Fixed Point Iteration	❌	❌	✅	❌	✅	❌	✅	3
Communication-Constrained Distributed Quantile Regression with Optimal Statistical Guarantees	✅	❌	❌	✅	❌	❌	✅	3
Community detection in sparse latent space models	✅	❌	✅	❌	✅	❌	✅	4
Conditions and Assumptions for Constraint-based Causal Structure Learning	❌	❌	❌	❌	❌	❌	❌	0
Constraint Reasoning Embedded Structured Prediction	✅	✅	✅	✅	❌	❌	✅	5
Contraction rates for sparse variational approximations in Gaussian process regression	❌	❌	❌	❌	❌	❌	✅	1
Convergence Guarantees for the Good-Turing Estimator	❌	❌	✅	❌	❌	❌	❌	1
Convergence Rates for Gaussian Mixtures of Experts	❌	❌	❌	❌	❌	❌	❌	0
D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data	❌	✅	✅	❌	✅	❌	✅	4
Darts: User-Friendly Modern Machine Learning for Time Series	❌	✅	✅	❌	❌	❌	✅	3
Data-Derived Weak Universal Consistency	❌	❌	❌	❌	❌	❌	❌	0
De-Sequentialized Monte Carlo: a parallel-in-time particle smoother	✅	✅	❌	❌	✅	❌	✅	4
Debiased Distributed Learning for Sparse Partial Linear Models in High Dimensions	❌	❌	❌	❌	❌	❌	✅	1
Decimated Framelet System on Graphs and Fast G-Framelet Transforms	✅	✅	✅	✅	✅	✅	✅	7
Deep Learning in Target Space	✅	✅	✅	✅	✅	✅	✅	7
Deep Limits and a Cut-Off Phenomenon for Neural Networks	❌	❌	❌	❌	❌	❌	✅	1
Deep Network Approximation: Achieving Arbitrary Accuracy with Fixed Number of Neurons	❌	❌	✅	✅	❌	❌	✅	3
Deepchecks: A Library for Testing and Validating Machine Learning Models and Data	❌	✅	❌	❌	❌	❌	❌	1
Dependent randomized rounding for clustering and partition systems with knapsack constraints	✅	❌	❌	❌	❌	❌	❌	1
Depth separation beyond radial functions	❌	❌	❌	❌	❌	❌	❌	0
Detecting Latent Communities in Network Formation Models	❌	❌	✅	❌	❌	❌	✅	2
Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior	✅	❌	✅	✅	❌	❌	✅	4
Distributed Bootstrap for Simultaneous Inference Under High Dimensionality	✅	✅	✅	✅	❌	❌	✅	5
Distributed Learning of Finite Gaussian Mixtures	✅	✅	✅	✅	✅	✅	✅	7
Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima	❌	❌	❌	❌	❌	❌	❌	0
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression	✅	✅	✅	✅	❌	✅	✅	6
Double Spike Dirichlet Priors for Structured Weighting	✅	✅	✅	✅	❌	❌	✅	5
DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python	❌	✅	❌	❌	❌	❌	✅	2
EV-GAN: Simulation of extreme events with ReLU neural networks	❌	❌	✅	❌	✅	✅	✅	4
Early Stopping for Iterative Regularization with General Loss Functions	❌	❌	❌	✅	❌	❌	❌	1
Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits	✅	✅	❌	❌	❌	❌	✅	3
Efficient Inference for Dynamic Flexible Interactions of Neural Populations	✅	✅	✅	✅	✅	❌	✅	6
Efficient Least Squares for Estimating Total Effects under Linearity and Causal Sufficiency	✅	✅	✅	❌	❌	✅	✅	5
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting	✅	❌	❌	❌	✅	❌	✅	3
EiGLasso for Scalable Sparse Kronecker-Sum Inverse Covariance Estimation	✅	✅	✅	❌	✅	❌	✅	5
Empirical Risk Minimization under Random Censorship	❌	✅	✅	✅	❌	❌	✅	4
Estimating Causal Effects under Network Interference with Bayesian Generalized Propensity Scores	✅	❌	✅	❌	❌	❌	✅	3
Estimating Density Models with Truncation Boundaries using Score Matching	❌	✅	✅	❌	✅	❌	✅	4
Estimation and inference on high-dimensional individualized treatment rule in observational data using split-and-pooled de-correlated score	✅	✅	❌	✅	❌	❌	✅	4
Evolutionary Variational Optimization of Generative Models	✅	✅	✅	❌	✅	❌	✅	5
Exact Partitioning of High-order Models with a Novel Convex Tensor Cone Relaxation	✅	❌	✅	❌	❌	❌	✅	3
Exact simulation of diffusion first exit times: algorithm acceleration	✅	✅	❌	❌	✅	❌	✅	4
Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks	✅	✅	✅	❌	❌	❌	✅	4
Expected Regret and Pseudo-Regret are Equivalent When the Optimal Arm is Unique	❌	❌	❌	❌	❌	❌	✅	1
Explicit Convergence Rates of Greedy and Random Quasi-Newton Methods	✅	❌	✅	❌	❌	❌	✅	3
Exploiting locality in high-dimensional Factorial hidden Markov models	✅	✅	✅	✅	✅	❌	✅	6
Extensions to the Proximal Distance Method of Constrained Optimization	✅	✅	✅	❌	✅	✅	✅	6
Fairness-Aware PAC Learning from Corrupted Data	❌	❌	❌	❌	❌	❌	❌	0
Fast Stagewise Sparse Factor Regression	✅	❌	✅	✅	❌	✅	✅	5
Fast and Robust Rank Aggregation against Model Misspecification	✅	❌	✅	❌	✅	❌	✅	4
Faster Randomized Interior Point Methods for Tall/Wide Linear Programs	✅	❌	✅	❌	✅	❌	✅	4
Foolish Crowds Support Benign Overfitting	❌	❌	❌	❌	❌	❌	❌	0
FuDGE: A Method to Estimate a Functional Differential Graph in a High-Dimensional Setting	✅	✅	✅	✅	❌	✅	✅	6
Fully General Online Imitation Learning	❌	✅	❌	❌	❌	❌	✅	2
Functional Linear Regression with Mixed Predictors	✅	✅	❌	✅	❌	❌	✅	4
Fundamental Limits and Tradeoffs in Invariant Representation Learning	❌	❌	✅	✅	❌	❌	❌	2
Gauss-Legendre Features for Gaussian Process Regression	❌	❌	✅	✅	✅	❌	✅	4
Gaussian Process Boosting	✅	✅	✅	✅	✅	✅	✅	7
Gaussian Process Parameter Estimation Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits	✅	❌	✅	✅	✅	✅	✅	6
Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression	❌	❌	❌	❌	❌	❌	✅	1
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects	✅	✅	✅	✅	❌	❌	✅	5
Generalized Ambiguity Decomposition for Ranking Ensemble Learning	✅	❌	✅	✅	❌	❌	✅	4
Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays	✅	✅	✅	✅	❌	❌	✅	5
Generalized Resubstitution for Classification Error Estimation	❌	❌	✅	✅	❌	❌	✅	3
Generalized Sparse Additive Models	✅	✅	✅	✅	✅	❌	✅	6
Getting Better from Worse: Augmented Bagging and A Cautionary Tale of Variable Importance	✅	❌	✅	✅	❌	❌	✅	4
Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch	✅	✅	✅	❌	❌	❌	✅	4
Globally Injective ReLU Networks	❌	❌	✅	❌	❌	❌	✅	2
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks	✅	✅	✅	✅	❌	❌	✅	5
Greedification Operators for Policy Optimization: Investigating Forward and Reverse KL Divergences	✅	❌	✅	❌	❌	❌	✅	3
Hamilton-Jacobi equations on graphs with applications to semi-supervised learning and data depth	❌	✅	✅	❌	❌	❌	✅	3
Handling Hard Affine SDP Shape Constraints in RKHSs	✅	✅	✅	✅	✅	❌	✅	6
IALE: Imitating Active Learner Ensembles	✅	✅	✅	✅	✅	❌	✅	6
Implicit Differentiation for Fast Hyperparameter Selection in Non-Smooth Convex Learning	✅	✅	✅	✅	❌	❌	✅	5
Improved Classification Rates for Localized SVMs	❌	❌	❌	❌	❌	❌	❌	0
Improved Generalization Bounds for Adversarially Robust Learning	✅	❌	❌	❌	❌	❌	❌	1
Improving Bayesian Network Structure Learning in the Presence of Measurement Error	✅	✅	✅	❌	❌	❌	✅	4
Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning	✅	✅	✅	✅	❌	❌	✅	5
Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification	✅	✅	✅	✅	❌	❌	✅	5
Inherent Tradeoffs in Learning Fair Representations	✅	❌	✅	✅	❌	❌	✅	4
Innovations Autoencoder and its Application in One-class Anomalous Sequence Detection	✅	❌	✅	❌	❌	❌	✅	3
Integral Autoencoder Network for Discretization-Invariant Learning	❌	✅	✅	✅	✅	❌	✅	5
Interlocking Backpropagation: Improving depthwise model-parallelism	❌	✅	✅	✅	✅	❌	✅	5
Interpolating Predictors in High-Dimensional Factor Regression	❌	❌	❌	❌	❌	❌	✅	1
InterpretDL: Explaining Deep Models in PaddlePaddle	❌	✅	❌	❌	❌	❌	❌	1
Interpretable Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings	✅	✅	✅	✅	❌	❌	✅	5
Interval-censored Hawkes processes	❌	❌	✅	✅	❌	❌	✅	3
Intrinsic Dimension Estimation Using Wasserstein Distance	❌	❌	✅	❌	❌	❌	❌	1
Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning	✅	✅	❌	❌	✅	❌	✅	4
Joint Continuous and Discrete Model Selection via Submodularity	❌	❌	✅	❌	✅	✅	✅	4
Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models	✅	❌	✅	✅	✅	❌	✅	5
Joint Inference of Multiple Graphs from Matrix Polynomials	❌	❌	✅	❌	❌	❌	✅	2
JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data	❌	✅	✅	❌	❌	❌	❌	2
Jump Gaussian Process Model for Estimating Piecewise Continuous Regression Functions	✅	❌	❌	✅	❌	❌	✅	3
KL-UCB-Switch: Optimal Regret Bounds for Stochastic Bandits from Both a Distribution-Dependent and a Distribution-Free Viewpoints	✅	❌	❌	❌	❌	❌	✅	2
Kernel Autocovariance Operators of Stationary Processes: Estimation and Convergence	❌	❌	❌	❌	❌	❌	❌	0
Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations	❌	❌	✅	❌	❌	❌	✅	2
Kernel Partial Correlation Coefficient --- a Measure of Conditional Dependence	✅	✅	✅	✅	❌	✅	✅	6
KoPA: Automated Kronecker Product Approximation	❌	❌	✅	❌	❌	❌	✅	2
LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data	✅	❌	✅	❌	✅	✅	✅	5
Learning Green's functions associated with time-dependent partial differential equations	❌	❌	❌	❌	❌	❌	❌	0
Learning Operators with Coupled Attention	✅	✅	✅	✅	✅	❌	✅	6
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training	✅	❌	✅	✅	❌	❌	✅	4
Learning Temporal Evolution of Spatial Dependence with Generalized Spatiotemporal Gaussian Process Models	❌	✅	✅	✅	❌	❌	✅	4
Learning from Noisy Pairwise Similarity and Unlabeled Data	✅	❌	✅	✅	✅	❌	✅	5
Learning linear non-Gaussian directed acyclic graph with diverging number of nodes	✅	❌	✅	❌	❌	❌	✅	3
Learning to Optimize: A Primer and A Benchmark	❌	✅	✅	✅	❌	❌	✅	4
Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence	✅	✅	✅	❌	❌	❌	✅	4
LinCDE: Conditional Density Estimation via Lindsey's Method	✅	✅	✅	✅	✅	✅	✅	7
Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps	✅	✅	✅	✅	✅	❌	❌	5
Logarithmic Regret for Episodic Continuous-Time Linear-Quadratic Reinforcement Learning over a Finite-Time Horizon	✅	❌	❌	❌	❌	❌	❌	1
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization	✅	❌	✅	✅	✅	❌	✅	5
MALTS: Matching After Learning to Stretch	❌	❌	✅	✅	❌	❌	✅	3
Machine Learning on Graphs: A Model and Comprehensive Taxonomy	❌	✅	✅	❌	❌	❌	❌	2
Manifold Coordinates with Physical Meaning	✅	✅	✅	❌	❌	❌	✅	4
Mappings for Marginal Probabilities with Applications to Models in Statistical Physics	❌	❌	❌	❌	❌	❌	✅	1
Matrix Completion with Covariate Information and Informative Missingness	❌	❌	✅	❌	❌	❌	✅	2
Maximum sampled conditional likelihood for informative subsampling	❌	❌	✅	✅	✅	❌	✅	4
Mean-field Analysis of Piecewise Linear Solutions for Wide ReLU Networks	❌	❌	❌	❌	❌	❌	✅	1
Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions	✅	✅	✅	✅	❌	❌	✅	5
Metrics of Calibration for Probabilistic Predictions	❌	✅	✅	❌	❌	❌	❌	2
Minimax Mixing Time of the Metropolis-Adjusted Langevin Algorithm for Log-Concave Sampling	✅	❌	❌	❌	❌	❌	✅	2
Minimax optimal approaches to the label shift problem in non-parametric settings	❌	✅	❌	✅	❌	❌	✅	3
Mitigating the Effects of Non-Identifiability on Inference for Bayesian Neural Networks with Latent Variables	❌	❌	✅	✅	❌	❌	✅	3
Model Averaging Is Asymptotically Better Than Model Selection For Prediction	❌	❌	❌	❌	❌	❌	❌	0
More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programming	✅	✅	✅	❌	✅	❌	✅	5
Multi-Agent Multi-Armed Bandits with Limited Communication	✅	❌	❌	❌	❌	❌	✅	2
Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism	✅	❌	❌	❌	❌	❌	❌	1
Multi-Task Dynamical Systems	✅	✅	✅	✅	✅	❌	✅	6
Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach	✅	❌	❌	❌	❌	❌	❌	1
Multiple-Splitting Projection Test for High-Dimensional Mean Vectors	✅	❌	✅	✅	❌	❌	✅	4
Multivariate Boosted Trees and Applications to Forecasting and Control	✅	✅	✅	✅	❌	❌	✅	5
MurTree: Optimal Decision Trees via Dynamic Programming and Search	✅	✅	✅	✅	✅	❌	✅	6
Mutual Information Constraints for Monte-Carlo Objectives to Prevent Posterior Collapse Especially in Language Modelling	❌	❌	✅	❌	❌	❌	✅	2
Near Optimality of Finite Memory Feedback Policies in Partially Observed Markov Decision Processes	❌	❌	❌	❌	❌	❌	✅	1
Network Regression with Graph Laplacians	❌	✅	✅	✅	✅	✅	✅	6
Neural Estimation of Statistical Divergences	❌	❌	❌	❌	❌	❌	❌	0
New Insights for the Multivariate Square-Root Lasso	✅	✅	✅	✅	❌	❌	✅	5
No Weighted-Regret Learning in Adversarial Bandits with Delays	✅	❌	❌	❌	❌	❌	❌	1
Non-asymptotic Properties of Individualized Treatment Rules from Sequentially Rule-Adaptive Trials	✅	❌	✅	✅	❌	❌	✅	4
Non-asymptotic and Accurate Learning of Nonlinear Dynamical Systems	❌	❌	❌	❌	❌	❌	✅	1
Nonconvex Matrix Completion with Linearly Parameterized Factors	❌	❌	❌	❌	❌	❌	✅	1
Nonparametric Neighborhood Selection in Graphical Models	✅	❌	✅	❌	❌	❌	✅	3
Nonparametric Principal Subspace Regression	❌	❌	✅	✅	❌	❌	❌	2
Nonparametric adaptive control and prediction: theory and randomized algorithms	❌	❌	❌	❌	❌	❌	✅	1
Nonstochastic Bandits with Composite Anonymous Feedback	✅	❌	❌	❌	❌	❌	❌	1
Novel Min-Max Reformulations of Linear Inverse Problems	✅	❌	✅	❌	❌	❌	✅	3
Nystrom Regularization for Time Series Forecasting	✅	✅	✅	✅	✅	✅	✅	7
OMLT: Optimization & Machine Learning Toolkit	❌	✅	✅	❌	❌	❌	❌	2
OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems	✅	✅	✅	❌	✅	❌	✅	5
On Acceleration for Convex Composite Minimization with Noise-Corrupted Gradients and Approximate Proximal Mapping	✅	❌	✅	❌	❌	❌	✅	3
On Biased Stochastic Gradient Estimation	✅	✅	✅	❌	❌	❌	✅	4
On Constraints in First-Order Optimization: A View from Non-Smooth Dynamical Systems	✅	✅	❌	❌	✅	✅	✅	5
On Generalizations of Some Distance Based Classifiers for HDLSS Data	❌	✅	✅	✅	❌	❌	✅	4
On Instrumental Variable Regression for Deep Offline Policy Evaluation	❌	✅	✅	✅	❌	❌	✅	4
On Low-rank Trace Regression under General Sampling Distribution	❌	✅	✅	✅	❌	❌	✅	4
On Mixup Regularization	✅	✅	✅	✅	❌	❌	✅	5
On Regularized Square-root Regression Problems: Distributionally Robust Interpretation and Fast Computations	✅	❌	✅	✅	✅	✅	✅	6
On the Approximation of Cooperative Heterogeneous Multi-Agent Reinforcement Learning (MARL) using Mean Field Control (MFC)	✅	❌	❌	❌	❌	❌	❌	1
On the Complexity of Approximating Multimarginal Optimal Transport	✅	❌	✅	❌	✅	✅	❌	4
On the Convergence Rates of Policy Gradient Methods	❌	❌	❌	❌	❌	❌	❌	0
On the Efficiency of Entropic Regularized Algorithms for Optimal Transport	✅	❌	✅	❌	❌	❌	✅	3
On the Robustness to Misspecification of α-posteriors and Their Variational Approximations	❌	❌	❌	❌	❌	❌	✅	1
Online Mirror Descent and Dual Averaging: Keeping Pace in the Dynamic Case	✅	❌	❌	❌	❌	❌	❌	1
Online Nonnegative CP-dictionary Learning for Markovian Data	✅	✅	✅	✅	❌	❌	✅	5
Optimal Transport for Stationary Markov Chains via Policy Iteration	✅	✅	✅	❌	❌	❌	✅	4
Optimality and Stability in Non-Convex Smooth Games	❌	❌	❌	❌	❌	❌	❌	0
Oracle Complexity in Nonsmooth Nonconvex Optimization	❌	❌	❌	❌	❌	❌	❌	0
Overparameterization of Deep ResNet: Zero Loss and Mean-field Analysis	❌	❌	❌	❌	❌	❌	❌	0
PAC Guarantees and Effective Algorithms for Detecting Novel Categories	✅	✅	✅	✅	❌	❌	✅	5
PECOS: Prediction for Enormous and Correlated Output Spaces	✅	✅	✅	✅	✅	❌	✅	6
Pathfinder: Parallel quasi-Newton variational inference	✅	✅	✅	❌	❌	✅	✅	5
Policy Evaluation and Temporal-Difference Learning in Continuous Time and Space: A Martingale Approach	❌	✅	❌	❌	✅	✅	✅	4
Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms	✅	✅	❌	❌	❌	❌	✅	3
Posterior Asymptotics for Boosted Hierarchical Dirichlet Process Mixtures	❌	❌	❌	❌	❌	❌	❌	0
Power Iteration for Tensor PCA	❌	❌	❌	❌	❌	❌	✅	1
Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks	❌	❌	✅	✅	❌	❌	✅	3
Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping	❌	✅	✅	✅	❌	❌	✅	4
Project and Forget: Solving Large-Scale Metric Constrained Problems	✅	✅	✅	✅	✅	✅	✅	7
Projected Robust PCA with Application to Smooth Image Recovery	✅	❌	✅	❌	✅	❌	✅	4
Projected Statistical Methods for Distributional Data on the Real Line with the Wasserstein Metric	❌	✅	✅	✅	❌	❌	✅	4
Projection-free Distributed Online Learning with Sublinear Communication Complexity	✅	❌	✅	✅	❌	❌	✅	4
Provable Tensor-Train Format Tensor Completion by Riemannian Optimization	✅	❌	❌	❌	❌	❌	✅	2
Quantile regression with ReLU Networks: Estimators and minimax rates	❌	✅	✅	❌	❌	❌	✅	3
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs	✅	✅	✅	✅	❌	❌	✅	5
Recovering shared structure from multiple networks with unknown edge distributions	❌	❌	✅	❌	❌	❌	✅	2
Recovery and Generalization in Over-Realized Dictionary Learning	✅	❌	❌	✅	❌	❌	✅	3
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction	✅	✅	✅	✅	✅	❌	✅	6
Regularized K-means Through Hard-Thresholding	❌	✅	✅	✅	❌	❌	✅	4
Regularized and Smooth Double Core Tensor Factorization for Heterogeneous Data	✅	✅	✅	✅	❌	❌	✅	5
Representation Learning for Maximization of MI, Nonlinear ICA and Nonlinear Subspaces with Robust Density Ratio Estimation	❌	❌	✅	✅	❌	❌	✅	3
ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models	❌	✅	❌	✅	✅	❌	✅	4
Rethinking Nonlinear Instrumental Variable Models through Prediction Validity	✅	❌	✅	✅	❌	❌	✅	4
Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning	✅	❌	✅	✅	❌	❌	N/A	3
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold	✅	❌	✅	✅	❌	❌	✅	4
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks	✅	✅	✅	❌	❌	❌	✅	4
Robust and scalable manifold learning via landmark diffusion for long-term medical signal processing	✅	✅	✅	✅	✅	✅	✅	7
SGD with Coordinate Sampling: Theory and Practice	✅	✅	✅	✅	✅	❌	✅	6
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization	❌	✅	✅	❌	❌	❌	❌	2
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation Networks	❌	✅	✅	✅	❌	❌	✅	4
Sampling Permutations for Shapley Value Estimation	✅	❌	✅	❌	✅	❌	✅	4
Scalable Gaussian-process regression and variable selection using Vecchia approximations	✅	✅	✅	✅	✅	❌	✅	6
Scalable and Efficient Hypothesis Testing with Random Forests	✅	❌	✅	✅	❌	❌	✅	4
Scaling Laws from the Data Manifold Dimension	❌	✅	✅	❌	❌	❌	✅	3
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Estimation from Incomplete Measurements	✅	✅	❌	❌	✅	❌	✅	4
Scaling-Translation-Equivariant Networks with Decomposed Convolutional Filters	❌	❌	✅	✅	❌	❌	✅	3
Score Matched Neural Exponential Families for Likelihood-Free Inference	✅	✅	✅	✅	❌	❌	✅	5
Selective Machine Learning of the Average Treatment Effect with an Invalid Instrumental Variable	✅	❌	✅	✅	❌	✅	❌	4
Self-Healing Robust Neural Networks via Closed-Loop Control	✅	✅	✅	✅	❌	❌	✅	5
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables	✅	✅	❌	❌	❌	❌	❌	2
Signature Moments to Characterize Laws of Stochastic Processes	❌	✅	✅	✅	✅	❌	✅	5
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States	✅	❌	❌	❌	❌	❌	✅	2
Simple and Optimal Stochastic Gradient Methods for Nonsmooth Nonconvex Optimization	✅	❌	❌	❌	❌	❌	❌	1
Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee	✅	❌	✅	✅	✅	✅	✅	6
Solving L1-regularized SVMs and Related Linear Programs: Revisiting the Effectiveness of Column and Constraint Generation	✅	✅	✅	❌	❌	✅	✅	5
Solving Large-Scale Sparse PCA to Certifiable (Near) Optimality	✅	✅	✅	❌	✅	✅	✅	6
Sparse Additive Gaussian Process Regression	✅	❌	✅	✅	✅	✅	✅	6
Sparse Continuous Distributions and Fenchel-Young Losses	✅	✅	✅	✅	❌	❌	✅	5
Spatial Multivariate Trees for Big Data Bayesian Regression	✅	✅	✅	✅	✅	✅	✅	7
Stable Classification	❌	❌	✅	✅	✅	✅	✅	5
Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors	❌	✅	✅	✅	✅	❌	✅	5
Statistical Optimality and Computational Efficiency of Nystrom Kernel PCA	❌	❌	❌	❌	❌	❌	❌	0
Statistical Optimality and Stability of Tangent Transform Algorithms in Logit Models	❌	❌	❌	❌	❌	❌	✅	1
Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification	❌	❌	❌	✅	❌	❌	✅	2
Stochastic DCA with Variance Reduction and Applications in Machine Learning	✅	❌	✅	❌	✅	❌	✅	4
Stochastic Zeroth-Order Optimization under Nonstationarity and Nonconvexity	✅	❌	❌	❌	❌	❌	✅	2
Stochastic subgradient for composite convex optimization with functional constraints	✅	❌	✅	✅	✅	❌	✅	5
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs	✅	✅	✅	❌	✅	✅	✅	6
Structure Learning for Directed Trees	✅	✅	✅	✅	❌	✅	✅	6
Structure-adaptive Manifold Estimation	✅	✅	✅	✅	❌	❌	✅	5
Sufficient reductions in regression with mixed predictors	❌	✅	✅	✅	❌	❌	✅	4
Sum of Ranked Range Loss for Supervised Learning	✅	✅	✅	✅	✅	✅	✅	7
Supervised Dimensionality Reduction and Visualization using Centroid-Encoder	✅	✅	✅	✅	❌	❌	✅	5
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity	✅	✅	✅	❌	✅	❌	✅	5
TFPnP: Tuning-free Plug-and-Play Proximal Algorithms with Applications to Inverse Imaging Problems	✅	✅	✅	❌	❌	❌	✅	4
Testing Whether a Learning Procedure is Calibrated	❌	❌	❌	✅	❌	❌	✅	2
The AIM and EM Algorithms for Learning from Coarse Data	✅	✅	❌	❌	❌	❌	✅	3
The Correlation-assisted Missing Data Estimator	❌	❌	✅	✅	❌	❌	✅	3
The EM Algorithm is Adaptively-Optimal for Unbalanced Symmetric Gaussian Mixtures	❌	❌	❌	❌	❌	❌	❌	0
The Geometry of Uniqueness, Sparsity and Clustering in Penalized Estimation	❌	❌	❌	❌	❌	❌	❌	0
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks	❌	❌	✅	✅	❌	❌	❌	2
The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks	❌	❌	❌	❌	❌	❌	❌	0
The Separation Capacity of Random Neural Networks	❌	❌	❌	❌	❌	❌	❌	0
The Two-Sided Game of Googol	❌	❌	❌	❌	❌	❌	❌	0
The Weighted Generalised Covariance Measure	✅	✅	✅	✅	❌	❌	✅	5
Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning	✅	❌	❌	❌	❌	❌	❌	1
Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data	❌	❌	✅	❌	❌	❌	✅	2
Three rates of convergence or separation via U-statistics in a dependent framework	❌	✅	❌	❌	❌	❌	✅	2
Tianshou: A Highly Modularized Deep Reinforcement Learning Library	❌	✅	✅	❌	❌	❌	❌	2
Toolbox for Multimodal Learn (scikit-multimodallearn)	❌	✅	✅	✅	❌	❌	✅	4
Topologically penalized regression on manifolds	❌	✅	✅	✅	✅	❌	❌	4
Total Stability of SVMs and Localized SVMs	❌	❌	❌	❌	❌	❌	❌	0
Toward Understanding Convolutional Neural Networks from Volterra Convolution Perspective	❌	✅	✅	❌	❌	❌	❌	2
Towards An Efficient Approach for the Nonconvex lp Ball Projection: Algorithm and Analysis	✅	✅	❌	❌	✅	❌	✅	4
Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration	✅	❌	✅	✅	❌	❌	✅	4
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent	❌	✅	✅	✅	❌	❌	✅	4
Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models	✅	❌	❌	❌	❌	❌	✅	2
Transfer Learning in Information Criteria-based Feature Selection	✅	✅	✅	✅	✅	✅	✅	7
Tree-Based Models for Correlated Data	✅	✅	✅	✅	❌	❌	✅	5
Tree-Values: Selective Inference for Regression Trees	✅	✅	✅	✅	❌	✅	✅	6
Tree-based Node Aggregation in Sparse Graphical Models	✅	✅	✅	✅	❌	❌	✅	5
Truncated Emphatic Temporal Difference Methods for Prediction and Control	✅	✅	✅	❌	❌	❌	✅	4
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions	✅	❌	✅	❌	❌	❌	✅	3
Two-mode Networks: Inference with as Many Parameters as Actors and Differential Privacy	✅	❌	✅	❌	✅	❌	✅	4
Unbiased estimators for random design regression	✅	❌	✅	❌	❌	❌	✅	3
Under-bagging Nearest Neighbors for Imbalanced Classification	✅	❌	✅	✅	✅	❌	✅	5
Underspecification Presents Challenges for Credibility in Modern Machine Learning	❌	❌	✅	✅	❌	✅	❌	3
Uniform deconvolution for Poisson Point Processes	❌	✅	✅	❌	✅	❌	✅	4
Universal Approximation Theorems for Differentiable Geometric Deep Learning	❌	❌	❌	❌	❌	❌	❌	0
Universal Approximation in Dropout Neural Networks	❌	❌	❌	❌	❌	❌	❌	0
Universal Approximation of Functions on Sets	❌	❌	❌	❌	❌	❌	❌	0
Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective	❌	❌	❌	❌	❌	❌	❌	0
Using Active Queries to Infer Symmetric Node Functions of Graph Dynamical Systems	✅	✅	✅	❌	✅	✅	✅	6
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features	❌	✅	✅	✅	✅	✅	✅	6
Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization	✅	❌	✅	❌	❌	❌	✅	3
Variational Inference in high-dimensional linear regression	❌	❌	❌	❌	❌	❌	❌	0
Vector-Valued Least-Squares Regression under Output Regularity Assumptions	✅	❌	✅	✅	❌	❌	✅	4
WarpDrive: Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU	❌	✅	❌	❌	✅	❌	❌	2
Weakly Supervised Disentangled Generative Causal Representation Learning	✅	✅	✅	✅	✅	❌	✅	6
When Hardness of Approximation Meets Hardness of Learning	❌	❌	❌	❌	❌	❌	❌	0
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint	✅	❌	❌	❌	❌	❌	❌	1
XAI Beyond Classification: Interpretable Neural Clustering	❌	✅	✅	✅	✅	✅	✅	6
abess: A Fast Best-Subset Selection Library in Python and R	❌	✅	✅	✅	✅	✅	✅	6
d3rlpy: An Offline Deep Reinforcement Learning Library	❌	✅	✅	✅	❌	❌	✅	4
ktrain: A Low-Code Library for Augmented Machine Learning	❌	✅	✅	❌	❌	❌	✅	3
solo-learn: A Library of Self-supervised Methods for Visual Representation Learning	❌	✅	✅	❌	❌	❌	✅	3
tntorch: Tensor Network Learning with PyTorch	❌	✅	❌	❌	✅	✅	❌	3