| $f$-MICL: Understanding and Generalizing InfoNCE-based Contrastive Learning |
✅ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| $k$-Mixup Regularization for Deep Learning via Optimal Transport |
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✅ |
❌ |
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5 |
| 3D-Aware Video Generation |
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3 |
| A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores |
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❌ |
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4 |
| A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles |
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❌ |
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❌ |
✅ |
5 |
| A Cubic Regularization Approach for Finding Local Minimax Points in Nonconvex Minimax Optimization |
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7 |
| A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize Range |
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❌ |
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6 |
| A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification |
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❌ |
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5 |
| A Free Lunch with Influence Functions? An Empirical Evaluation of Influence Functions for Average Treatment Effect Estimation |
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❌ |
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4 |
| A Halfspace-Mass Depth-Based Method for Adversarial Attack Detection |
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❌ |
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6 |
| A Kernel Perspective on Behavioural Metrics for Markov Decision Processes |
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✅ |
✅ |
❌ |
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❌ |
❌ |
2 |
| A Measure of the Complexity of Neural Representations based on Partial Information Decomposition |
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✅ |
❌ |
❌ |
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4 |
| A Modulation Layer to Increase Neural Network Robustness Against Data Quality Issues |
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6 |
| A Proximal Algorithm for Sampling |
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❌ |
✅ |
2 |
| A Ranking Game for Imitation Learning |
✅ |
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✅ |
❌ |
❌ |
❌ |
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4 |
| A Reproducible and Realistic Evaluation of Partial Domain Adaptation Methods |
❌ |
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❌ |
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4 |
| A Revenue Function for Comparison-Based Hierarchical Clustering |
❌ |
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❌ |
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❌ |
✅ |
3 |
| A Robust Backpropagation-Free Framework for Images |
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❌ |
✅ |
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6 |
| A Simulation Environment and Reinforcement Learning Method for Waste Reduction |
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❌ |
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6 |
| A Stochastic Proximal Polyak Step Size |
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✅ |
❌ |
❌ |
✅ |
5 |
| A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning |
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❌ |
❌ |
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4 |
| A Survey on Causal Discovery Methods for I.I.D. and Time Series Data |
❌ |
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❌ |
✅ |
❌ |
❌ |
3 |
| A Survey on Transformers in Reinforcement Learning |
❌ |
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❌ |
❌ |
❌ |
❌ |
0 |
| A Survey on the Possibilities & Impossibilities of AI-generated Text Detection |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Systematic Approach to Universal Random Features in Graph Neural Networks |
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❌ |
✅ |
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✅ |
✅ |
✅ |
5 |
| A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models |
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✅ |
✅ |
❌ |
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❌ |
✅ |
4 |
| A Unified View of Masked Image Modeling |
❌ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Variational Perspective on Generative Flow Networks |
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✅ |
2 |
| A probabilistic Taylor expansion with Gaussian processes |
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❌ |
❌ |
❌ |
✅ |
1 |
| AI-SARAH: Adaptive and Implicit Stochastic Recursive Gradient Methods |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| AP: Selective Activation for De-sparsifying Pruned Networks |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| About the Cost of Central Privacy in Density Estimation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Accelerated Quality-Diversity through Massive Parallelism |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Accelerating Batch Active Learning Using Continual Learning Techniques |
✅ |
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❌ |
✅ |
❌ |
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4 |
| Achieving Risk Control in Online Learning Settings |
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✅ |
❌ |
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6 |
| Achieving the Pareto Frontier of Regret Minimization and Best Arm Identification in Multi-Armed Bandits |
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❌ |
❌ |
❌ |
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3 |
| Action Poisoning Attacks on Linear Contextual Bandits |
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4 |
| Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task |
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✅ |
❌ |
❌ |
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4 |
| Active Learning of Ordinal Embeddings: A User Study on Football Data |
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4 |
| Adaptive Compression for Communication-Efficient Distributed Training |
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6 |
| Adaptive Hyperparameter Selection for Differentially Private Gradient Descent |
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2 |
| Adaptive patch foraging in deep reinforcement learning agents |
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❌ |
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2 |
| Addressing caveats of neural persistence with deep graph persistence |
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❌ |
❌ |
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5 |
| Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness |
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❌ |
❌ |
❌ |
3 |
| Agent-State Construction with Auxiliary Inputs |
❌ |
✅ |
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3 |
| An Adaptive Half-Space Projection Method for Stochastic Optimization Problems with Group Sparse Regularization |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| An Analysis of Model-Based Reinforcement Learning From Abstracted Observations |
✅ |
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❌ |
1 |
| An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow |
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❌ |
❌ |
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2 |
| An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms |
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✅ |
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3 |
| An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-MDP |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel |
❌ |
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❌ |
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❌ |
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3 |
| Analyzing Deep PAC-Bayesian Learning with Neural Tangent Kernel: Convergence, Analytic Generalization Bound, and Efficient Hyperparameter Selection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Approximating Naive Bayes on Unlabelled Categorical Data |
✅ |
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❌ |
❌ |
✅ |
2 |
| Assisted Learning for Organizations with Limited Imbalanced Data |
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❌ |
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4 |
| Assisting Human Decisions in Document Matching |
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❌ |
✅ |
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5 |
| Assuming Locally Equal Calibration Errors for Non-Parametric Multiclass Calibration |
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❌ |
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6 |
| Asymptotic Analysis of Conditioned Stochastic Gradient Descent |
❌ |
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✅ |
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❌ |
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2 |
| Attacking Perceptual Similarity Metrics |
✅ |
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✅ |
❌ |
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5 |
| Attention Beats Concatenation for Conditioning Neural Fields |
❌ |
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✅ |
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4 |
| Attentional-Biased Stochastic Gradient Descent |
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✅ |
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❌ |
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5 |
| Augmented Language Models: a Survey |
❌ |
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✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Automated Detection of Causal Inference Opportunities: Regression Discontinuity Subgroup Discovery |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| BIGRoC: Boosting Image Generation via a Robust Classifier |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Bandwidth Enables Generalization in Quantum Kernel Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bayesian Causal Bandits with Backdoor Adjustment Prior |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Optimization with Informative Covariance |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Quadrature for Neural Ensemble Search |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Bayesian Transformed Gaussian Processes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Benchmarking Continuous Time Models for Predicting Multiple Sclerosis Progression |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Benchmarks and Algorithms for Offline Preference-Based Reward Learning |
✅ |
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✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Benchmarks for Physical Reasoning AI |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Benefits of Max Pooling in Neural Networks: Theoretical and Experimental Evidence |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Better Theory for SGD in the Nonconvex World |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Beyond Boundaries: A Novel Data-Augmentation Discourse for Open Domain Generalization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Beyond Information Gain: An Empirical Benchmark for Low-Switching-Cost Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Beyond Intuition: Rethinking Token Attributions inside Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Bidirectional View based Consistency Regularization for Semi-Supervised Domain Adaptation |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Binary Classification under Local Label Differential Privacy Using Randomized Response Mechanisms |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Black-Box Batch Active Learning for Regression |
❌ |
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❌ |
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❌ |
✅ |
4 |
| Black-Box Prompt Learning for Pre-trained Language Models |
✅ |
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✅ |
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❌ |
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6 |
| Bounded Space Differentially Private Quantiles |
✅ |
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❌ |
❌ |
❌ |
❌ |
2 |
| Bounding generalization error with input compression: An empirical study with infinite-width networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Bridging Graph Position Encodings for Transformers with Weighted Graph-Walking Automata |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bridging performance gap between minimal and maximal SVM models |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Bridging the Gap Between Target Networks and Functional Regularization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| CAE v2: Context Autoencoder with CLIP Latent Alignment |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Calibrating and Improving Graph Contrastive Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Can Pruning Improve Certified Robustness of Neural Networks? |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Catastrophic overfitting can be induced with discriminative non-robust features |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Causal Parrots: Large Language Models May Talk Causality But Are Not Causal |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Causal Reinforcement Learning: A Survey |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Causally-guided Regularization of Graph Attention Improves Generalizability |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Chasing Better Deep Image Priors between Over- and Under-parameterization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| ChemSpacE: Interpretable and Interactive Chemical Space Exploration |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Clustering using Approximate Nearest Neighbour Oracles |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Communication-Efficient Distributionally Robust Decentralized Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Comparative Generalization Bounds for Deep Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Complementary Sparsity: Accelerating Sparse CNNs with High Accuracy on General-Purpose Computing Platforms |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Computationally-efficient initialisation of GPs: The generalised variogram method |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Conditional Generative Models are Provably Robust: Pointwise Guarantees for Bayesian Inverse Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Conditional Permutation Invariant Flows |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Conditional Sampling of Variational Autoencoders via Iterated Approximate Ancestral Sampling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Conformal prediction under ambiguous ground truth |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Consistent Collaborative Filtering via Tensor Decomposition |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Constrained Parameter Inference as a Principle for Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Containing a spread through sequential learning: to exploit or to explore? |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contextual Combinatorial Multi-output GP Bandits with Group Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contextualize Me – The Case for Context in Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Continual Learning by Modeling Intra-Class Variation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Contrastive Attraction and Contrastive Repulsion for Representation Learning |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Contrastive Search Is What You Need For Neural Text Generation |
❌ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Controlling Neural Network Smoothness for Neural Algorithmic Reasoning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Costs and Benefits of Fair Regression |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Cox-Hawkes: doubly stochastic spatiotemporal Poisson processes |
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❌ |
❌ |
✅ |
5 |
| Cross-client Label Propagation for Transductive and Semi-Supervised Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Cross-validation for Geospatial Data: Estimating Generalization Performance in Geostatistical Problems |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Cyclic and Randomized Stepsizes Invoke Heavier Tails in SGD than Constant Stepsize |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Cyclophobic Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| DEUP: Direct Epistemic Uncertainty Prediction |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DORA: Exploring Outlier Representations in Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DP-LFlow: Differentially Private Latent Flow for Scalable Sensitive Image Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DPVIm: Differentially Private Variational Inference Improved |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DSpar: An Embarrassingly Simple Strategy for Efficient GNN training and inference via Degree-based Sparsification |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Data Distillation: A Survey |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Data Models for Dataset Drift Controls in Machine Learning With Optical Images |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Data pruning and neural scaling laws: fundamental limitations of score-based algorithms |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Data-Free Diversity-Based Ensemble Selection for One-Shot Federated Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Deep Double Descent via Smooth Interpolation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Deep Operator Learning Lessens the Curse of Dimensionality for PDEs |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
1 |
| Deep Plug-and-Play Clustering with Unknown Number of Clusters |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Defense Against Reward Poisoning Attacks in Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Denise: Deep Robust Principal Component Analysis for Positive Semidefinite Matrices |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Detecting danger in gridworlds using Gromov’s Link Condition |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Detecting incidental correlation in multimodal learning via latent variable modeling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Diagnostic Tool for Out-of-Sample Model Evaluation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Differentiable Logic Machines |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Differentially Private Diffusion Models |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Differentially Private Image Classification from Features |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Differentially Private Optimizers Can Learn Adversarially Robust Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Differentially private partitioned variational inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Dirichlet Mechanism for Differentially Private KL Divergence Minimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DisCo: Improving Compositional Generalization in Visual Reasoning through Distribution Coverage |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Discretization Invariant Networks for Learning Maps between Neural Fields |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Distributed Architecture Search Over Heterogeneous Distributions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Distributed Newton-Type Methods with Communication Compression and Bernoulli Aggregation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Distributionally Robust Classification on a Data Budget |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Do Vision-Language Pretrained Models Learn Composable Primitive Concepts? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DoCoM: Compressed Decentralized Optimization with Near-Optimal Sample Complexity |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Does ‘Deep Learning on a Data Diet’ reproduce? Overall yes, but GraNd at Initialization does not |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Dr-Fairness: Dynamic Data Ratio Adjustment for Fair Training on Real and Generated Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DreamEdit: Subject-driven Image Editing |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Dropped Scheduled Task: Mitigating Negative Transfer in Multi-task Learning using Dynamic Task Dropping |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Dual PatchNorm |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dual Representation Learning for Out-of-distribution Detection |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Subgoal-based Exploration via Bayesian Optimization |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dynamics Adapted Imitation Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ECG Representation Learning with Multi-Modal EHR Data |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Early Stopping for Deep Image Prior |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Inference With Model Cascades |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Empirical Limitations of the NTK for Understanding Scaling Laws in Deep Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Empirical Study on Optimizer Selection for Out-of-Distribution Generalization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Equivariant MuZero |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Error bounds and dynamics of bootstrapping in actor-critic reinforcement learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Estimating Differential Equations from Temporal Point Processes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Euclidean-Norm-Induced Schatten-p Quasi-Norm Regularization for Low-Rank Tensor Completion and Tensor Robust Principal Component Analysis |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Evaluating Human-Language Model Interaction |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Event Tables for Efficient Experience Replay |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Execution-based Code Generation using Deep Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Expected Worst Case Regret via Stochastic Sequential Covering |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Explaining Visual Counterfactual Explainers |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Extreme Masking for Learning Instance and Distributed Visual Representations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| FASTRAIN-GNN: Fast and Accurate Self-Training for Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| FLUID: A Unified Evaluation Framework for Flexible Sequential Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fair Kernel Regression through Cross-Covariance Operators |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Fair and Useful Cohort Selection |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| FairGrad: Fairness Aware Gradient Descent |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fairness and robustness in anti-causal prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale with MinDiff Loss |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Kernel Methods for Generic Lipschitz Losses via $p$-Sparsified Sketches |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fast Slate Policy Optimization: Going Beyond Plackett-Luce |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fast Treatment Personalization with Latent Bandits in Fixed-Confidence Pure Exploration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast&Fair: Training Acceleration and Bias Mitigation for GNNs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Faster Training of Neural ODEs Using Gauß–Legendre Quadrature |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| FedDAG: Federated DAG Structure Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Federated High-Dimensional Online Decision Making |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Federated Learning under Covariate Shifts with Generalization Guarantees |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Federated Learning under Partially Disjoint Data via Manifold Reshaping |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Federated Minimax Optimization with Client Heterogeneity |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Finding Competence Regions in Domain Generalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Finding Neurons in a Haystack: Case Studies with Sparse Probing |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Finding and Only Finding Differential Nash Equilibria by Both Pretending to be a Follower |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Finite-Time Analysis of Decentralized Single-Timescale Actor-Critic |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Foiling Explanations in Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Fourier Features in Reinforcement Learning with Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Fusion of Global and Local Knowledge for Personalized Federated Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GIT-Net: Generalized Integral Transform for Operator Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GPS++: Reviving the Art of Message Passing for Molecular Property Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GSR: A Generalized Symbolic Regression Approach |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gated Domain Units for Multi-source Domain Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Generalizability of Adversarial Robustness Under Distribution Shifts |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Generalization as Dynamical Robustness--The Role of Riemannian Contraction in Supervised Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
1 |
| Generalization bounds for Kernel Canonical Correlation Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generating Adversarial Examples with Task Oriented Multi-Objective Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Global Contrastive Learning for Long-Tailed Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Gradient Masked Averaging for Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Gradient-adjusted Incremental Target Propagation Provides Effective Credit Assignment in Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Graph Neural Networks Designed for Different Graph Types: A Survey |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Graph Neural Networks for Temporal Graphs: State of the Art, Open Challenges, and Opportunities |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GraphPNAS: Learning Probabilistic Graph Generators for Neural Architecture Search |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Greedier is Better: Selecting Multiple Neighbors per Iteration for Sparse Subspace Clustering |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Group Fairness in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time series |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hidden Heterogeneity: When to Choose Similarity-Based Calibration |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| High Fidelity Neural Audio Compression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| High-Modality Multimodal Transformer: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Holistic Evaluation of Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Homomorphic Self-Supervised Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| How to Reuse and Compose Knowledge for a Lifetime of Tasks: A Survey on Continual Learning and Functional Composition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| HypUC: Hyperfine Uncertainty Calibration with Gradient- boosted Corrections for Reliable Regression on Imbalanced Electrocardiograms |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| IBIA: An Incremental Build-Infer-Approximate Framework for Approximate Inference of Partition Function |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| ILPO-MP: Mode Priors Prevent Mode Collapse when Imitating Latent Policies from Observations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Identification of Negative Transfers in Multitask Learning Using Surrogate Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Identifying latent distances with Finslerian geometry |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Image Compression with Product Quantized Masked Image Modeling |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Image retrieval outperforms diffusion models on data augmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Implicit Ensemble Training for Efficient and Robust Multiagent Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improved Differentially Private Riemannian Optimization: Fast Sampling and Variance Reduction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Group Robustness via Classifier Retraining on Independent Splits |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improved Overparametrization Bounds for Global Convergence of SGD for Shallow Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Improved baselines for vision-language pre-training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improved identification accuracy in equation learning via comprehensive $\boldsymbol{R^2}$-elimination and Bayesian model selection |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improving Continual Learning by Accurate Gradient Reconstructions of the Past |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Improving Differentially Private SGD via Randomly Sparsified Gradients |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving Generalization with Approximate Factored Value Functions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Improving Native CNN Robustness with Filter Frequency Regularization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| In search of projectively equivariant networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Inducing Meaningful Units from Character Sequences with Dynamic Capacity Slot Attention |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Inherent Limits on Topology-Based Link Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Integrating Bayesian Network Structure into Residual Flows and Variational Autoencoders |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Interpretable Mixture of Experts |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Intrinsic Dimension for Large-Scale Geometric Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Invariant Feature Coding using Tensor Product Representation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Invariant Structure Learning for Better Generalization and Causal Explainability |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Inverse Scaling: When Bigger Isn't Better |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Invertible Hierarchical Generative Model for Images |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Jacobian-based Causal Discovery with Nonlinear ICA |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| JiangJun: Mastering Xiangqi by Tackling Non-Transitivity in Two-Player Zero-Sum Games |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| L-SVRG and L-Katyusha with Adaptive Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LEAD: Min-Max Optimization from a Physical Perspective |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Label Noise-Robust Learning using a Confidence-Based Sieving Strategy |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Latent State Models of Training Dynamics |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Layerwise Bregman Representation Learning of Neural Networks with Applications to Knowledge Distillation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learn the Time to Learn: Replay Scheduling in Continual Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learned Thresholds Token Merging and Pruning for Vision Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Augmentation Distributions using Transformed Risk Minimization |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Graph Structure from Convolutional Mixtures |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Learning Identity-Preserving Transformations on Data Manifolds |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Interpolations between Boltzmann Densities |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Multiscale Non-stationary Causal Structures |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Object-Centric Neural Scattering Functions for Free-viewpoint Relighting and Scene Composition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Learning Representations for Pixel-based Control: What Matters and Why? |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Symbolic Rules for Reasoning in Quasi-Natural Language |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning domain-specific causal discovery from time series |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning from time-dependent streaming data with online stochastic algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning representations that are closed-form Monge mapping optimal with application to domain adaptation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Boost Resilience of Complex Networks via Neural Edge Rewiring |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Incentivize Improvements from Strategic Agents |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning to Look by Self-Prediction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning to Optimize Quasi-Newton Methods |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Learning to correct spectral methods for simulating turbulent flows |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Learning to reconstruct signals from binary measurements alone |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning-to-defer for sequential medical decision-making under uncertainty |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Leveraging Demonstrations with Latent Space Priors |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Lifelong Reinforcement Learning with Modulating Masks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Lightweight Learner for Shared Knowledge Lifelong Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Limitation of Characterizing Implicit Regularization by Data-independent Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Linearized Relative Positional Encoding |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Linking Neural Collapse and L2 Normalization with Improved Out-of-Distribution Detection in Deep Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Local Advantage Networks for Multi-Agent Reinforcement Learning in Dec-POMDPs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Local Function Complexity for Active Learning via Mixture of Gaussian Processes |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Logistic-Normal Likelihoods for Heteroscedastic Label Noise |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Long-term Forecasting with TiDE: Time-series Dense Encoder |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MASIF: Meta-learned Algorithm Selection using Implicit Fidelity Information |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| MaMMUT: A Simple Architecture for Joint Learning for MultiModal Tasks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Machine Explanations and Human Understanding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Mean-Field Control based Approximation of Multi-Agent Reinforcement Learning in Presence of a Non-decomposable Shared Global State |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta Continual Learning on Graphs with Experience Replay |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Meta-Calibration: Learning of Model Calibration Using Differentiable Expected Calibration Error |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Meta-Learning via Classifier(-free) Diffusion Guidance |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Mind the Gap: Mitigating the Distribution Gap in Graph Few-shot Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Minorization-Maximization for Learning Determinantal Point Processes |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Mitigating Confirmation Bias in Semi-supervised Learning via Efficient Bayesian Model Averaging |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mitigating Real-World Distribution Shifts in the Fourier Domain |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Mixed effects in machine learning – A flexible mixedML framework to add random effects to supervised machine learning regression |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Modelling sequential branching dynamics with a multivariate branching Gaussian process |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Modular Deep Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Monotone deep Boltzmann machines |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-annotator Deep Learning: A Probabilistic Framework for Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-label Node Classification On Graph-Structured Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-objective Bayesian Optimization with Heuristic Objectives for Biomedical and Molecular Data Analysis Workflows |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Multimodal Language Learning for Object Retrieval in Low Data Regimes in the Face of Missing Modalities |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multiscale Causal Structure Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| NOFLITE: Learning to Predict Individual Treatment Effect Distributions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Named Tensor Notation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Neighborhood Gradient Mean: An Efficient Decentralized Learning Method for Non-IID Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Causal Structure Discovery from Interventions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Collapse: A Review on Modelling Principles and Generalization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Monge Map estimation and its applications |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Networks beyond explainability: Selective inference for sequence motifs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Neural Ordinary Differential Equations for Modeling Epidemic Spreading |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural Shape Compiler: A Unified Framework for Transforming between Text, Point Cloud, and Program |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Non-Stationary Contextual Pricing with Safety Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nonconvex-nonconcave min-max optimization on Riemannian manifolds |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Not All Causal Inference is the Same |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Novel Class Discovery for Long-tailed Recognition |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Numerical Accounting in the Shuffle Model of Differential Privacy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Numerical Data Imputation for Multimodal Data Sets: A Probabilistic Nearest-Neighbor Kernel Density Approach |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Off-Policy Evaluation with Out-of-Sample Guarantees |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Offline Reinforcement Learning with Additional Covering Distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Offline Reinforcement Learning with Mixture of Deterministic Policies |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| On Adaptivity in Quantum Testing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Average-Case Error Bounds for Kernel-Based Bayesian Quadrature |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Averaging ROC Curves |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On Perfect Clustering for Gaussian Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On a continuous time model of gradient descent dynamics and instability in deep learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Convergence and Calibration of Deep Learning with Differential Privacy |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| On the Efficacy of Differentially Private Few-shot Image Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Gradient Formula for learning Generative Models with Regularized Optimal Transport Costs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Robustness of Dataset Inference |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| On the Sample Complexity of Lipschitz Constant Estimation |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the Statistical Complexity of Estimation and Testing under Privacy Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the infinite-depth limit of finite-width neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the special role of class-selective neurons in early training |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| One-Round Active Learning through Data Utility Learning and Proxy Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| One-Step Distributional Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Learning for Prediction via Covariance Fitting: Computation, Performance and Robustness |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online Min-max Problems with Non-convexity and Non-stationarity |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online Optimal Tracking of Linear Systems with Adversarial Disturbances |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online model selection by learning how compositional kernels evolve |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| OpenCon: Open-world Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Optimal Convergence Rates of Deep Convolutional Neural Networks: Additive Ridge Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal Threshold Labeling for Ordinal Regression Methods |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Optimistic Optimization of Gaussian Process Samples |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Optimum-statistical Collaboration Towards General and Efficient Black-box Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Overcoming Resource Constraints in Federated Learning: Large Models Can Be Trained with only Weak Clients |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PAC-Bayes Generalisation Bounds for Heavy-Tailed Losses through Supermartingales |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| PAVI: Plate-Amortized Variational Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| PCPs: Patient Cardiac Prototypes to Probe AI-based Medical Diagnoses, Distill Datasets, and Retrieve Patients |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| POMRL: No-Regret Learning-to-Plan with Increasing Horizons |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| PRUDEX-Compass: Towards Systematic Evaluation of Reinforcement Learning in Financial Markets |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pairwise Learning with Adaptive Online Gradient Descent |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Parameter Efficient Node Classification on Homophilic Graphs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Pareto Actor-Critic for Equilibrium Selection in Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Partial Optimal Transport for Support Subset Selection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Partition-Based Active Learning for Graph Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Patches Are All You Need? |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Personalized Federated Learning with Communication Compression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Personalized Federated Learning: A Unified Framework and Universal Optimization Techniques |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Physics informed neural networks for elliptic equations with oscillatory differential operators |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Policy Gradient Algorithms Implicitly Optimize by Continuation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| PolyViT: Co-training Vision Transformers on Images, Videos and Audio |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pre-trained Perceptual Features Improve Differentially Private Image Generation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Predicting Out-of-Domain Generalization with Neighborhood Invariance |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Privacy Budget Tailoring in Private Data Analysis |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Privacy-Preserving Energy-Based Generative Models for Marginal Distribution Protection |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Private GANs, Revisited |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Private Multi-Task Learning: Formulation and Applications to Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Probing Predictions on OOD Images via Nearest Categories |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Projected Randomized Smoothing for Certified Adversarial Robustness |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Proportional Fairness in Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| ProtoCaps: A Fast and Non-Iterative Capsule Network Routing Method |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Provably Convergent Policy Optimization via Metric-aware Trust Region Methods |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Provably Personalized and Robust Federated Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Proximal Curriculum for Reinforcement Learning Agents |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Quantum Policy Iteration via Amplitude Estimation and Grover Search – Towards Quantum Advantage for Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RCT Rejection Sampling for Causal Estimation Evaluation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| RECLIP: Resource-efficient CLIP by Training with Small Images |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| RIFLE: Imputation and Robust Inference from Low Order Marginals |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| RIGNN: A Rationale Perspective for Semi-supervised Open-world Graph Classification |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RLTF: Reinforcement Learning from Unit Test Feedback |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Reducing Predictive Feature Suppression in Resource-Constrained Contrastive Image-Caption Retrieval |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Regret Bounds for Satisficing in Multi-Armed Bandit Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regularized Training of Intermediate Layers for Generative Models for Inverse Problems |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reinforcement Learning with Delayed, Composite, and Partially Anonymous Reward |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reinforcement Teaching |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Relating graph auto-encoders to linear models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Releasing Graph Neural Networks with Differential Privacy Guarantees |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reliable Active Learning via Influence Functions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Replay-enhanced Continual Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Resmax: An Alternative Soft-Greedy Operator for Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Retiring $\Delta \text{DP}$: New Distribution-Level Metrics for Demographic Parity |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reusable Options through Gradient-based Meta Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Revisiting Hidden Representations in Transfer Learning for Medical Imaging |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting Sparsity Hunting in Federated Learning: Why does Sparsity Consensus Matter? |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Revisiting Topic-Guided Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Revisiting adversarial training for the worst-performing class |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rewiring with Positional Encodings for Graph Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Robust Hybrid Learning With Expert Augmentation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Robust Multi-Agent Reinforcement Learning with State Uncertainty |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Robustness through Data Augmentation Loss Consistency |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| SANTA: Source Anchoring Network and Target Alignment for Continual Test Time Adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SC2 Benchmark: Supervised Compression for Split Computing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SHAP-XRT: The Shapley Value Meets Conditional Independence Testing |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| SIESTA: Efficient Online Continual Learning with Sleep |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SMILE: Sample-to-feature Mixup for Efficient Transfer Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| SPADE: Semi-supervised Anomaly Detection under Distribution Mismatch |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scalable Deep Compressive Sensing |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scalable Stochastic Gradient Riemannian Langevin Dynamics in Non-Diagonal Metrics |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Self-Attention in Colors: Another Take on Encoding Graph Structure in Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-Supervised Graph Representation Learning for Neuronal Morphologies |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-Supervision is All You Need for Solving Rubik’s Cube |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Self-supervised Learning for Segmentation and Quantification of Dopamine Neurons in Parkinson’s Disease |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Semantic Representations of Mathematical Expressions in a Continuous Vector Space |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Semantic Self-adaptation: Enhancing Generalization with a Single Sample |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Semi-Supervised Single Domain Generalization with Label-Free Adversarial Data Augmentation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Separable Self-attention for Mobile Vision Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sequential Query Encoding for Complex Query Answering on Knowledge Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sharper Rates and Flexible Framework for Nonconvex SGD with Client and Data Sampling |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Signed Graph Neural Networks: A Frequency Perspective |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Smoothed Differential Privacy |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Sobolev Spaces, Kernels and Discrepancies over Hyperspheres |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Soft Diffusion: Score Matching with General Corruptions |
✅ |
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✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SolidGen: An Autoregressive Model for Direct B-rep Synthesis |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Solving Nonconvex-Nonconcave Min-Max Problems exhibiting Weak Minty Solutions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Solving a Special Type of Optimal Transport Problem by a Modified Hungarian Algorithm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Some Remarks on Identifiability of Independent Component Analysis in Restricted Function Classes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Spectral Regularization Allows Data-frugal Learning over Combinatorial Spaces |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Spectral learning of Bernoulli linear dynamical systems models for decision-making |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Stacking Diverse Architectures to Improve Machine Translation |
❌ |
✅ |
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❌ |
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❌ |
✅ |
4 |
| StarCoder: may the source be with you! |
❌ |
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✅ |
✅ |
❌ |
✅ |
5 |
| Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning |
✅ |
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❌ |
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6 |
| Stochastic Constrained DRO with a Complexity Independent of Sample Size |
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4 |
| Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize |
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5 |
| Stochastic gradient updates yield deep equilibrium kernels |
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4 |
| Straggler-Resilient Personalized Federated Learning |
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3 |
| Structured Low-Rank Tensors for Generalized Linear Models |
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4 |
| Subgraph Permutation Equivariant Networks |
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5 |
| Successor Feature Representations |
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4 |
| Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks |
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6 |
| Supervised Knowledge May Hurt Novel Class Discovery Performance |
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5 |
| Synthetic Data from Diffusion Models Improves ImageNet Classification |
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4 |
| TSMixer: An All-MLP Architecture for Time Series Forecast-ing |
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5 |
| TabCBM: Concept-based Interpretable Neural Networks for Tabular Data |
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4 |
| Tackling Provably Hard Representative Selection via Graph Neural Networks |
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6 |
| Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity |
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0 |
| Target Propagation via Regularized Inversion for Recurrent Neural Networks |
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6 |
| Task Weighting in Meta-learning with Trajectory Optimisation |
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5 |
| Teaching Smaller Language Models To Generalise To Unseen Compositional Questions |
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4 |
| Temperature check: theory and practice for training models with softmax-cross-entropy losses |
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2 |
| Test-Time Adaptation for Visual Document Understanding |
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5 |
| The (Un)Scalability of Informed Heuristic Function Estimation in NP-Hard Search Problems |
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4 |
| The Analysis of the Expected Change in the Classification Probability of the Predicted Label |
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4 |
| The ConceptARC Benchmark: Evaluating Understanding and Generalization in the ARC Domain |
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2 |
| The Eigenlearning Framework: A Conservation Law Perspective on Kernel Ridge Regression and Wide Neural Networks |
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4 |
| The Geometry of Mixability |
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0 |
| The Kernel Density Integral Transformation |
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5 |
| The Low-Rank Simplicity Bias in Deep Networks |
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5 |
| The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus |
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4 |
| The Multiquadric Kernel for Moment-Matching Distributional Reinforcement Learning |
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3 |
| The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science |
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5 |
| The Robustness Limits of SoTA Vision Models to Natural Variation |
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2 |
| The Score-Difference Flow for Implicit Generative Modeling |
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2 |
| The Stack: 3 TB of permissively licensed source code |
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2 |
| The Vendi Score: A Diversity Evaluation Metric for Machine Learning |
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4 |
| Tight conditions for when the NTK approximation is valid |
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0 |
| TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection |
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5 |
| Towards Better Generalization with Flexible Representation of Multi-Module Graph Neural Networks |
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4 |
| Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks |
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4 |
| Towards Fair Video Summarization |
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6 |
| Towards Large Scale Transfer Learning for Differentially Private Image Classification |
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5 |
| Towards Multi-spatiotemporal-scale Generalized PDE Modeling |
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4 |
| Towards Optimization-Friendly Binary Neural Network |
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7 |
| Towards Stability of Autoregressive Neural Operators |
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6 |
| Towards a Defense Against Federated Backdoor Attacks Under Continuous Training |
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4 |
| Towards a General Transfer Approach for Policy-Value Networks |
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5 |
| Towards a More Rigorous Science of Blindspot Discovery in Image Classification Models |
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5 |
| Training DNNs Resilient to Adversarial and Random Bit-Flips by Learning Quantization Ranges |
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5 |
| Training Data Size Induced Double Descent For Denoising Feedforward Neural Networks and the Role of Training Noise |
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4 |
| Training Vision-Language Transformers from Captions |
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6 |
| Training with Mixed-Precision Floating-Point Assignments |
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5 |
| TransFool: An Adversarial Attack against Neural Machine Translation Models |
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6 |
| Transductive Decoupled Variational Inference for Few-Shot Classification |
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6 |
| Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer |
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4 |
| Transformer for Partial Differential Equations’ Operator Learning |
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6 |
| Transframer: Arbitrary Frame Prediction with Generative Models |
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2 |
| Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport |
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5 |
| Transport with Support: Data-Conditional Diffusion Bridges |
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5 |
| Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings |
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5 |
| Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows |
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5 |
| Turning a Curse into a Blessing: Enabling In-Distribution-Data-Free Backdoor Removal via Stabilized Model Inversion |
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4 |
| Two-Level Actor-Critic Using Multiple Teachers |
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3 |
| U-NO: U-shaped Neural Operators |
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2 |
| U-Statistics for Importance-Weighted Variational Inference |
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3 |
| UnIVAL: Unified Model for Image, Video, Audio and Language Tasks |
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5 |
| UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography |
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5 |
| Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior |
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6 |
| Uncovering Unique Concept Vectors through Latent Space Decomposition |
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3 |
| Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient |
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3 |
| Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification |
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4 |
| Understanding Curriculum Learning in Policy Optimization for Online Combinatorial Optimization |
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5 |
| Understanding Noise-Augmented Training for Randomized Smoothing |
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4 |
| Understanding Self-Supervised Pretraining with Part-Aware Representation Learning |
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3 |
| Understanding and Simplifying Architecture Search in Spatio-Temporal Graph Neural Networks |
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4 |
| Understanding convolution on graphs via energies |
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5 |
| Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods |
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5 |
| Unifying physical systems’ inductive biases in neural ODE using dynamics constraints |
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4 |
| Universal Graph Continual Learning |
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3 |
| Unsupervised Discovery and Composition of Object Light Fields |
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4 |
| Unsupervised Domain Adaptation via Minimized Joint Error |
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4 |
| Using Confounded Data in Latent Model-Based Reinforcement Learning |
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3 |
| Using Representation Expressiveness and Learnability to Evaluate Self-Supervised Learning Methods |
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4 |
| V1T: large-scale mouse V1 response prediction using a Vision Transformer |
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5 |
| VN-Transformer: Rotation-Equivariant Attention for Vector Neurons |
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4 |
| Variational Causal Dynamics: Discovering Modular World Models from Interventions |
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4 |
| Variational Elliptical Processes |
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5 |
| ViViT: Curvature Access Through The Generalized Gauss-Newton’s Low-Rank Structure |
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5 |
| Visualizing the Diversity of Representations Learned by Bayesian Neural Networks |
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3 |
| VoLTA: Vision-Language Transformer with Weakly-Supervised Local-Feature Alignment |
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6 |
| Vulnerability-Aware Instance Reweighting For Adversarial Training |
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3 |
| WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series |
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3 |
| Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing |
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5 |
| Weight-balancing fixes and flows for deep learning |
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2 |
| Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings |
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5 |
| When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making |
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2 |
| When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning |
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6 |
| Workflow Discovery from Dialogues in the Low Data Regime |
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5 |
| Worst-case Feature Risk Minimization for Data-Efficient Learning |
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4 |
| Wrapped $\beta$-Gaussians with compact support for exact probabilistic modeling on manifolds |
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4 |
| You Only Transfer What You Share: Intersection-Induced Graph Transfer Learning for Link Prediction |
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5 |
| Zero-shot Node Classification with Graph Contrastive Embedding Network |
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4 |
| lo-fi: distributed fine-tuning without communication |
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5 |
| mL-BFGS: A Momentum-based L-BFGS for Distributed Large-scale Neural Network Optimization |
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5 |