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..

Transactions on Machine Learning Research (TMLR) - 2022

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
TMLR	2022	216	0.61	4.23	96.3%	48.08%

Search Papers

	Pseudocode	Open Source Code	Open Datasets	Dataset Splits	Hardware Specification	Software Dependencies	Experiment Setup
A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey	❌	❌	✅	✅	✅	✅	✅	5
A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful	❌	✅	❌	✅	❌	❌	✅	3
A Generalist Agent	❌	❌	✅	✅	✅	❌	✅	4
A Note on "Assessing Generalization of SGD via Disagreement"	❌	✅	✅	✅	❌	❌	✅	4
A Rigorous Study Of The Deep Taylor Decomposition	✅	✅	✅	❌	❌	❌	❌	3
A Self-Supervised Framework for Function Learning and Extrapolation	❌	❌	❌	✅	❌	❌	✅	2
A Simple Convergence Proof of Adam and Adagrad	❌	❌	✅	❌	✅	❌	✅	3
A Snapshot of the Frontiers of Client Selection in Federated Learning	❌	❌	✅	❌	❌	❌	❌	1
A Stochastic Optimization Framework for Fair Risk Minimization	✅	✅	✅	✅	❌	❌	✅	5
A Unified Domain Adaptation Framework with Distinctive Divergence Analysis	❌	❌	✅	✅	✅	❌	✅	4
A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges	✅	✅	✅	✅	❌	❌	❌	4
A geometrical connection between sparse and low-rank matrices and its application to manifold learning	❌	❌	✅	✅	❌	❌	✅	3
ANCER: Anisotropic Certification via Sample-wise Volume Maximization	✅	✅	✅	✅	✅	❌	✅	6
Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance	✅	✅	✅	❌	✅	❌	✅	5
Adversarial Feature Augmentation and Normalization for Visual Recognition	✅	✅	✅	✅	✅	❌	✅	6
Algorithms and Theory for Supervised Gradual Domain Adaptation	❌	❌	✅	❌	❌	❌	❌	1
An Efficient One-Class SVM for Novelty Detection in IoT	✅	✅	✅	✅	✅	✅	✅	7
An approximate sampler for energy-based models with divergence diagnostics	✅	❌	✅	❌	❌	❌	✅	3
An empirical study of implicit regularization in deep offline RL	✅	❌	✅	✅	❌	❌	✅	4
Approximate Policy Iteration with Bisimulation Metrics	✅	❌	❌	❌	✅	❌	✅	3
Approximating 1-Wasserstein Distance with Trees	✅	✅	✅	✅	✅	❌	✅	6
Attentive Walk-Aggregating Graph Neural Networks	✅	✅	✅	✅	✅	❌	✅	6
Attribute Prediction as Multiple Instance Learning	❌	❌	✅	✅	❌	❌	✅	3
Auto-Lambda: Disentangling Dynamic Task Relationships	❌	✅	✅	❌	❌	❌	✅	3
Bayesian Methods for Constraint Inference in Reinforcement Learning	✅	❌	✅	❌	❌	❌	✅	3
Behind the Machine’s Gaze: Neural Networks with Biologically-inspired Constraints Exhibit Human-like Visual Attention	❌	❌	✅	❌	❌	❌	✅	2
Benchmarking Progress to Infant-Level Physical Reasoning in AI	❌	✅	✅	✅	❌	❌	❌	3
Benchmarking and Analyzing Unsupervised Network Representation Learning and the Illusion of Progress	❌	✅	✅	✅	✅	❌	✅	5
Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation	✅	✅	✅	❌	❌	❌	❌	3
Boosting Search Engines with Interactive Agents	✅	✅	✅	✅	✅	❌	✅	6
Bridging Offline and Online Experimentation: Constraint Active Search for Deployed Performance Optimization	✅	❌	✅	✅	✅	❌	✅	5
COIN++: Neural Compression Across Modalities	❌	✅	✅	✅	✅	✅	✅	6
Calibrated Selective Classification	✅	✅	✅	✅	❌	❌	✅	5
Can You Win Everything with A Lottery Ticket?	❌	✅	✅	✅	❌	❌	✅	4
Causal Feature Selection via Orthogonal Search	✅	✅	✅	✅	❌	❌	✅	5
Centroids Matching: an efficient Continual Learning approach operating in the embedding space	❌	✅	✅	❌	❌	❌	✅	3
Clustering units in neural networks: upstream vs downstream information	✅	✅	✅	❌	❌	❌	✅	4
CoCa: Contrastive Captioners are Image-Text Foundation Models	❌	❌	✅	✅	✅	❌	✅	4
Collaborative Algorithms for Online Personalized Mean Estimation	✅	✅	✅	❌	❌	❌	✅	4
Competition over data: how does data purchase affect users?	✅	✅	✅	✅	❌	❌	✅	5
Completeness and Coherence Learning for Fast Arbitrary Style Transfer	❌	❌	✅	❌	✅	❌	✅	3
Complex-Valued Autoencoders for Object Discovery	❌	✅	✅	✅	✅	✅	✅	6
Concave Utility Reinforcement Learning with Zero-Constraint Violations	✅	❌	❌	❌	✅	❌	✅	3
Conformal Prediction Intervals with Temporal Dependence	✅	✅	✅	✅	❌	❌	✅	5
Controllable Generative Modeling via Causal Reasoning	✅	❌	✅	❌	❌	❌	✅	3
Convergence of denoising diffusion models under the manifold hypothesis	❌	❌	❌	❌	❌	❌	❌	0
Counterfactual Learning with Multioutput Deep Kernels	❌	✅	✅	✅	✅	❌	✅	5
DHA: End-to-End Joint Optimization of Data Augmentation Policy, Hyper-parameter and Architecture	✅	❌	✅	❌	✅	✅	❌	4
DR-DSGD: A Distributionally Robust Decentralized Learning Algorithm over Graphs	✅	❌	✅	✅	❌	❌	✅	4
Data Leakage in Federated Averaging	✅	✅	✅	✅	✅	❌	✅	6
Decoder Denoising Pretraining for Semantic Segmentation	❌	❌	✅	✅	✅	❌	✅	4
Decoding EEG With Spiking Neural Networks on Neuromorphic Hardware	❌	✅	✅	✅	✅	❌	✅	5
Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter	❌	✅	✅	✅	✅	❌	✅	5
Deep Classifiers with Label Noise Modeling and Distance Awareness	✅	✅	✅	✅	✅	❌	✅	6
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure	✅	✅	✅	❌	✅	❌	✅	5
Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach	✅	✅	✅	✅	✅	✅	✅	7
Deformation Robust Roto-Scale-Translation Equivariant CNNs	❌	✅	✅	✅	❌	❌	✅	4
Degradation Attacks on Certifiably Robust Neural Networks	✅	✅	✅	✅	✅	❌	✅	6
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models	❌	✅	✅	❌	❌	❌	✅	3
Did I do that? Blame as a means to identify controlled effects in reinforcement learning	❌	❌	✅	✅	❌	❌	✅	3
Differentiable Model Compression via Pseudo Quantization Noise	❌	✅	✅	✅	✅	❌	✅	5
Differentially Private Stochastic Expectation Propagation	✅	✅	✅	✅	❌	❌	✅	5
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents	✅	✅	✅	✅	✅	❌	✅	6
Diffusion Models for Video Prediction and Infilling	✅	✅	✅	✅	✅	❌	✅	6
Direct Molecular Conformation Generation	❌	✅	✅	✅	✅	❌	✅	5
Distributed Stochastic Algorithms for High-rate Streaming Principal Component Analysis	✅	❌	✅	❌	❌	❌	✅	3
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks	❌	✅	✅	✅	❌	❌	✅	4
Do ReLU Networks Have An Edge When Approximating Compactly-Supported Functions?	❌	❌	❌	❌	❌	❌	❌	0
Do better ImageNet classifiers assess perceptual similarity better?	✅	✅	✅	✅	❌	❌	✅	5
Does Entity Abstraction Help Generative Transformers Reason?	❌	❌	✅	✅	✅	✅	✅	5
Domain Invariant Adversarial Learning	✅	✅	✅	✅	❌	❌	✅	5
Domain-invariant Feature Exploration for Domain Generalization	❌	✅	✅	✅	❌	❌	✅	4
Efficient CDF Approximations for Normalizing Flows	✅	✅	✅	❌	✅	❌	✅	5
Efficient Gradient Flows in Sliced-Wasserstein Space	✅	❌	✅	✅	✅	❌	✅	5
Emergent Abilities of Large Language Models	❌	❌	✅	❌	❌	❌	❌	1
Enhanced gradient-based MCMC in discrete spaces	✅	❌	✅	❌	❌	✅	✅	4
Ensembles of Classifiers: a Bias-Variance Perspective	✅	❌	✅	✅	❌	❌	✅	4
Equivariant Mesh Attention Networks	✅	✅	✅	✅	❌	❌	✅	5
Estimating Potential Outcome Distributions with Collaborating Causal Networks	❌	✅	✅	✅	✅	❌	✅	5
Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning	✅	✅	✅	✅	✅	❌	✅	6
Explicit Group Sparse Projection with Applications to Deep Learning and NMF	✅	✅	✅	❌	❌	❌	✅	4
Exploring Efficient Few-shot Adaptation for Vision Transformers	❌	✅	✅	✅	❌	❌	✅	4
Exploring Generative Neural Temporal Point Process	✅	✅	✅	✅	✅	❌	✅	6
Exploring the Learning Mechanisms of Neural Division Modules	❌	✅	✅	✅	✅	❌	✅	5
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images	✅	✅	✅	✅	❌	❌	✅	5
Extracting Local Reasoning Chains of Deep Neural Networks	❌	❌	✅	✅	✅	❌	✅	4
FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data	✅	✅	✅	✅	❌	❌	✅	5
Fail-Safe Adversarial Generative Imitation Learning	❌	✅	✅	✅	❌	❌	❌	3
Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed	❌	✅	✅	✅	❌	❌	✅	4
Faking Interpolation Until You Make It	✅	✅	✅	✅	✅	❌	✅	6
Fast and Accurate Spreading Process Temporal Scale Estimation	✅	❌	✅	❌	❌	❌	✅	3
FedShuffle: Recipes for Better Use of Local Work in Federated Learning	✅	❌	✅	✅	❌	✅	✅	5
Finding and Fixing Spurious Patterns with Explanations	✅	✅	✅	✅	❌	❌	✅	5
Fingerprints of Super Resolution Networks	❌	❌	✅	✅	❌	❌	✅	3
Flipped Classroom: Effective Teaching for Time Series Forecasting	❌	✅	✅	✅	❌	❌	✅	4
Fourier Sensitivity and Regularization of Computer Vision Models	✅	❌	✅	✅	❌	❌	✅	4
From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality	✅	❌	✅	❌	❌	❌	✅	3
GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation	✅	✅	✅	✅	✅	❌	✅	6
GIT: A Generative Image-to-text Transformer for Vision and Language	❌	❌	✅	✅	❌	❌	✅	3
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets	❌	✅	✅	✅	✅	❌	✅	5
Generative Adversarial Neural Operators	✅	✅	✅	❌	❌	❌	✅	4
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution	❌	❌	✅	✅	✅	❌	✅	4
Greedy Bayesian Posterior Approximation with Deep Ensembles	✅	✅	✅	✅	✅	❌	✅	6
HEAT: Hyperedge Attention Networks	✅	✅	✅	❌	❌	❌	✅	4
High Fidelity Visualization of What Your Self-Supervised Representation Knows About	❌	✅	✅	✅	❌	❌	❌	3
How Expressive are Transformers in Spectral Domain for Graphs?	❌	❌	✅	✅	✅	❌	✅	4
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers	❌	✅	✅	✅	✅	❌	✅	5
INR-V: A Continuous Representation Space for Video-based Generative Tasks	❌	✅	✅	✅	✅	❌	✅	5
Identifiable Deep Generative Models via Sparse Decoding	✅	✅	✅	✅	✅	❌	✅	6
Identifying Causal Structure in Dynamical Systems	✅	✅	❌	❌	❌	❌	✅	3
If your data distribution shifts, use self-learning	❌	✅	✅	✅	✅	✅	✅	6
Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization	❌	✅	✅	✅	❌	❌	✅	4
Incorporating Sum Constraints into Multitask Gaussian Processes	✅	✅	✅	✅	✅	❌	✅	6
Indiscriminate Data Poisoning Attacks on Neural Networks	✅	❌	✅	✅	✅	❌	✅	5
Infinitely wide limits for deep Stable neural networks: sub-linear, linear and super-linear activation functions	❌	❌	❌	❌	❌	❌	❌	0
Integrating Rankings into Quantized Scores in Peer Review	✅	✅	✅	❌	❌	❌	✅	4
Interpretable Node Representation with Attribute Decoding	✅	❌	✅	✅	❌	❌	✅	4
Iterative State Estimation in Non-linear Dynamical Systems Using Approximate Expectation Propagation	✅	✅	✅	❌	❌	❌	✅	4
LIMIS: Locally Interpretable Modeling using Instance-wise Subsampling	✅	❌	✅	❌	✅	❌	✅	4
Lazy vs hasty: linearization in deep networks impacts learning schedule based on example difficulty	❌	❌	✅	✅	❌	❌	✅	3
Learning Accurate Decision Trees with Bandit Feedback via Quantized Gradient Descent	✅	✅	✅	✅	❌	✅	✅	6
Learning Algorithms for Markovian Bandits:\\Is Posterior Sampling more Scalable than Optimism?	✅	✅	✅	❌	❌	❌	✅	4
Learning Two-Step Hybrid Policy for Graph-Based Interpretable Reinforcement Learning	❌	❌	✅	✅	❌	❌	✅	3
Learning the Transformer Kernel	❌	✅	✅	✅	✅	❌	✅	5
Learning to Switch Among Agents in a Team via 2-Layer Markov Decision Processes	✅	❌	✅	❌	❌	❌	✅	3
Linear algebra with transformers	❌	✅	❌	✅	✅	❌	✅	4
Local Kernel Ridge Regression for Scalable, Interpolating, Continuous Regression	✅	❌	✅	✅	❌	❌	✅	4
Lookback for Learning to Branch	❌	❌	✅	✅	✅	✅	✅	5
MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based Depth	✅	✅	✅	✅	✅	❌	✅	6
Mace: A flexible framework for membership privacy estimation in generative models	✅	❌	✅	✅	❌	❌	✅	4
Max-Affine Spline Insights Into Deep Network Pruning	✅	✅	✅	✅	✅	❌	✅	6
Mean-Field Langevin Dynamics : Exponential Convergence and Annealing	❌	✅	❌	❌	❌	❌	✅	2
Meta-Learning Sparse Compression Networks	❌	❌	✅	❌	❌	❌	✅	2
Mitigating Catastrophic Forgetting in Spiking Neural Networks through Threshold Modulation	✅	✅	✅	✅	❌	❌	✅	5
MixTailor: Mixed Gradient Aggregation for Robust Learning Against Tailored Attacks	❌	❌	✅	✅	✅	❌	✅	4
Modeling Bounded Rationality in Multi-Agent Simulations Using Rationally Inattentive Reinforcement Learning	❌	❌	❌	❌	❌	❌	✅	1
Modeling Object Dissimilarity for Deep Saliency Prediction	❌	✅	✅	✅	✅	❌	✅	5
Momentum Capsule Networks	✅	✅	✅	❌	❌	❌	✅	4
Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexities	✅	❌	❌	❌	❌	❌	✅	2
Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias	❌	❌	✅	❌	❌	❌	❌	1
Multitask Online Mirror Descent	✅	❌	✅	❌	❌	❌	✅	3
NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes	❌	❌	✅	✅	✅	❌	✅	4
No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RL	✅	❌	❌	❌	❌	❌	✅	2
NoiLin: Improving adversarial training and correcting stereotype of noisy labels	✅	✅	✅	✅	✅	❌	✅	6
Non-Deterministic Behavior of Thompson Sampling with Linear Payoffs and How to Avoid It	✅	✅	✅	✅	❌	❌	❌	4
Nonparametric Learning of Two-Layer ReLU Residual Units	✅	✅	✅	✅	✅	✅	✅	7
Nonstationary Reinforcement Learning with Linear Function Approximation	✅	❌	❌	❌	✅	❌	✅	3
Object-aware Cropping for Self-Supervised Learning	❌	❌	✅	✅	❌	❌	✅	3
On Characterizing the Trade-off in Invariant Representation Learning	❌	✅	✅	✅	❌	❌	✅	4
On Noise Abduction for Answering Counterfactual Queries: A Practical Outlook	❌	✅	✅	❌	✅	❌	✅	4
On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning	✅	❌	✅	✅	❌	❌	✅	4
On Robustness to Missing Video for Audiovisual Speech Recognition	❌	✅	✅	✅	❌	❌	✅	4
On Sample Complexity of Offline Reinforcement Learning with Deep ReLU Networks in Besov Spaces	✅	❌	❌	❌	❌	❌	❌	1
On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning	❌	✅	✅	❌	❌	❌	✅	3
On the Adversarial Robustness of Vision Transformers	❌	✅	✅	✅	❌	❌	✅	4
On the Choice of Interpolation Scheme for Neural CDEs	❌	✅	✅	✅	✅	❌	✅	5
On the Convergence of Shallow Neural Network Training with Randomly Masked Neurons	✅	❌	✅	❌	❌	❌	✅	3
On the Near-Optimality of Local Policies in Large Cooperative Multi-Agent Reinforcement Learning	✅	✅	❌	❌	❌	❌	✅	3
On the Origins of the Block Structure Phenomenon in Neural Network Representations	❌	❌	✅	✅	❌	❌	✅	3
On the Paradox of Certified Training	❌	❌	✅	✅	✅	❌	✅	4
On the link between conscious function and general intelligence in humans and machines	❌	❌	❌	❌	❌	❌	❌	0
Online Coresets for Parameteric and Non-Parametric Bregman Clustering	✅	❌	✅	❌	❌	❌	✅	3
Online Double Oracle	✅	✅	✅	❌	❌	❌	✅	4
Optimal Client Sampling for Federated Learning	✅	✅	✅	❌	❌	❌	✅	4
Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks	✅	❌	✅	✅	✅	❌	✅	5
Optimizing Intermediate Representations of Generative Models for Phase Retrieval	❌	❌	✅	✅	✅	❌	✅	4
Practicality of generalization guarantees for unsupervised domain adaptation with neural networks	❌	❌	✅	✅	❌	❌	✅	3
Probabilistic Autoencoder	❌	✅	✅	✅	❌	❌	✅	4
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning	❌	✅	✅	❌	✅	❌	✅	4
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning	✅	✅	✅	✅	❌	❌	✅	5
Ranking Recovery under Privacy Considerations	❌	❌	❌	❌	❌	❌	✅	1
Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning	✅	✅	✅	✅	✅	❌	✅	6
Recurrent networks, hidden states and beliefs in partially observable environments	✅	❌	✅	❌	❌	❌	✅	3
Reinventing Policy Iteration under Time Inconsistency	✅	❌	❌	❌	❌	❌	✅	2
Representation Alignment in Neural Networks	❌	✅	✅	✅	✅	❌	✅	5
Robust and Data-efficient Q-learning by Composite Value-estimation	✅	✅	✅	❌	❌	❌	✅	4
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning	✅	✅	✅	✅	❌	❌	✅	5
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation	❌	❌	✅	✅	✅	❌	✅	4
Secure Domain Adaptation with Multiple Sources	✅	✅	✅	❌	✅	❌	✅	5
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection	✅	❌	✅	✅	✅	❌	✅	5
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning	✅	❌	✅	✅	✅	❌	✅	5
Sequentially learning the topological ordering of directed acyclic graphs with likelihood ratio scores	✅	✅	✅	✅	✅	❌	✅	6
Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation	❌	✅	✅	✅	❌	❌	❌	3
Sparse Coding with Multi-layer Decoders using Variance Regularization	✅	✅	✅	✅	✅	❌	✅	6
Sparse MoEs meet Efficient Ensembles	❌	✅	✅	✅	❌	❌	✅	4
Stable and Interpretable Unrolled Dictionary Learning	✅	✅	✅	✅	✅	✅	✅	7
Stochastic Douglas-Rachford Splitting for Regularized Empirical Risk Minimization: Convergence, Mini-batch, and Implementation	✅	❌	✅	✅	❌	❌	✅	4
Structural Learning in Artificial Neural Networks: A Neural Operator Perspective	❌	❌	❌	❌	❌	❌	❌	0
Structured Uncertainty in the Observation Space of Variational Autoencoders	❌	✅	✅	❌	✅	❌	✅	4
Symbolic Regression is NP-hard	❌	❌	❌	❌	❌	❌	❌	0
Systematically and efficiently improving $k$-means initialization by pairwise-nearest-neighbor smoothing	✅	✅	✅	❌	✅	✅	✅	6
TITRATED: Learned Human Driving Behavior without Infractions via Amortized Inference	✅	❌	✅	✅	✅	❌	✅	5
TLDR: Twin Learning for Dimensionality Reduction	✅	✅	✅	✅	✅	❌	✅	6
Teacher’s pet: understanding and mitigating biases in distillation	❌	❌	✅	✅	❌	❌	✅	3
Teaching Models to Express Their Uncertainty in Words	❌	❌	❌	✅	❌	❌	✅	2
The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning	❌	❌	✅	✅	❌	✅	✅	4
The Fundamental Limits of Neural Networks for Interval Certified Robustness	❌	❌	❌	❌	❌	❌	❌	0
The Graph Cut Kernel for Ranked Data	✅	✅	✅	✅	✅	❌	✅	6
Time Series Alignment with Global Invariances	✅	❌	✅	✅	❌	❌	✅	4
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning	✅	✅	✅	✅	❌	❌	✅	5
Uncertainty-Based Active Learning for Reading Comprehension	✅	❌	✅	✅	❌	❌	✅	4
Understanding AdamW through Proximal Methods and Scale-Freeness	✅	❌	✅	❌	❌	❌	✅	3
Understanding Linearity of Cross-Lingual Word Embedding Mappings	✅	✅	✅	❌	❌	❌	✅	4
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities	❌	✅	✅	✅	❌	✅	✅	5
Unimodal Likelihood Models for Ordinal Data	❌	✅	✅	✅	❌	❌	✅	4
Unsupervised Dense Information Retrieval with Contrastive Learning	❌	✅	✅	✅	❌	❌	✅	4
Unsupervised Learning of Neurosymbolic Encoders	✅	✅	✅	✅	✅	❌	✅	6
Unsupervised Mismatch Localization in Cross-Modal Sequential Data with Application to Mispronunciations Localization	✅	❌	✅	✅	❌	❌	✅	4
Unsupervised Network Embedding Beyond Homophily	✅	✅	✅	❌	❌	❌	❌	3
Using unsupervised learning to detect broken symmetries, with relevance to searches for parity violation in nature.	❌	✅	✅	❌	❌	❌	✅	3
Variational Disentanglement for Domain Generalization	✅	❌	✅	✅	✅	✅	✅	6
Weight Expansion: A New Perspective on Dropout and Generalization	✅	❌	✅	✅	❌	❌	✅	4
Your Policy Regularizer is Secretly an Adversary	❌	❌	❌	❌	❌	❌	✅	1
ZerO Initialization: Initializing Neural Networks with only Zeros and Ones	✅	✅	✅	✅	❌	❌	✅	5
Zero-Shot Learning with Common Sense Knowledge Graphs	✅	✅	✅	✅	✅	❌	✅	6
sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification	✅	✅	✅	✅	✅	❌	✅	6