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Theory

4,950 papers

Papers per year

Papers

Investigating a Generalization of Probabilistic Material Implication and Bayesian Conditionals UAI 2023
Residual-based error bound for physics-informed neural networks UAI 2023
The Shrinkage-Delinkage Trade-off: an Analysis of Factorized Gaussian Approximations for Variational Inference UAI 2023
On Minimizing the Impact of Dataset Shifts on Actionable Explanations UAI 2023
On Testability and Goodness of Fit Tests in Missing Data Models UAI 2023
Simple Transferability Estimation for Regression Tasks UAI 2023
Incentivizing honest performative predictions with proper scoring rules UAI 2023
On the limitations of Markovian rewards to express multi-objective, risk-sensitive, and modal tasks UAI 2023
Why Out-of-Distribution detection experiments are not reliable - subtle experimental details muddle the OOD detector rankings UAI 2023
Bidirectional Attention as a Mixture of Continuous Word Experts UAI 2023
Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? UAI 2023
Mitigating Transformer Overconfidence via Lipschitz Regularization UAI 2023
Certified Defense for Content Based Image Retrieval WACV 2023
Hyperdimensional Feature Fusion for Out-of-Distribution Detection WACV 2023
Towards Equivariant Optical Flow Estimation With Deep Learning WACV 2023
Why do tree-based models still outperform deep learning on typical tabular data? NIPS 2022
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs NIPS 2022
Multi-Class $H$-Consistency Bounds NIPS 2022
Sparsity in Continuous-Depth Neural Networks NIPS 2022
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning NIPS 2022
Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting NIPS 2022
Learning in Observable POMDPs, without Computationally Intractable Oracles NIPS 2022
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games NIPS 2022
What You See is What You Get: Principled Deep Learning via Distributional Generalization NIPS 2022
Not too little, not too much: a theoretical analysis of graph (over)smoothing NIPS 2022