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Optimization & Theory

43,292 papers (3 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Optimization (17,731) Learning Theory (6,809) Bayesian Inference (6,267) Theory (5,579) Statistical Learning (4,564) Neural Network Optimization (4,155) Stochastic Processes (2,948) Stochastic Methods (1,283) Loss Functions (1,220) Distributed Learning (1,202) Online Algorithms (1,013) Statistics (822) Generalization (376) Information Theory (370) Sample Complexity (282) Sparse Optimization (241) Game Theory (151) Convex Optimization (132) Optimal Transport (104) Robust Optimization (84) Distributed Optimization (73) Approximation Algorithms (33)

Papers per year

1
52
352
828
3,646
5,854
2000
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Papers (including subtopics)

Interpolating Classifiers Make Few Mistakes JMLR 2023 Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics ICML 2023 Interpretable and Fair Boolean Rule Sets via Column Generation JMLR 2023 Interpretable differencing of machine learning models UAI 2023 Interpretable Geometric Deep Learning via Learnable Randomness Injection ICLR 2023 Interpretable (not just posthoc-explainable) heterogeneous survivors bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions MLHC 2023 Interpreting Positional Information in Perspective of Word Order ACL 2023 Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning L4DC 2023 Intersection Coordination with Priority-Based Search for Autonomous Vehicles AAAI 2023 Interval Bound Interpolation for Few-shot Learning with Few Tasks ICML 2023 Interventional Bag Multi-Instance Learning on Whole-Slide Pathological Images CVPR 2023 Intractability of Learning the Discrete Logarithm with Gradient-Based Methods ACML 2023 Intriguing Properties of Quantization at Scale NeurIPS 2023 Intrinsic dimensionality and generalization properties of the R-norm inductive bias COLT 2023 Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts NeurIPS 2023 Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs ICML 2023 Introducing Competition To Boost the Transferability of Targeted Adversarial Examples Through Clean Feature Mixup CVPR 2023 Invariance in Policy Optimisation and Partial Identifiability in Reward Learning ICML 2023 Invariant Learning via Probability of Sufficient and Necessary Causes NeurIPS 2023 Invariant Representations with Stochastically Quantized Neural Networks AAAI 2023 Inverse-Reference Priors for Fisher Regularization of Bayesian Neural Networks AAAI 2023 Inverse Reinforcement Learning with the Average Reward Criterion NeurIPS 2023 Inverse Scaling Can Become U-Shaped EMNLP 2023 Inverting the Imaging Process by Learning an Implicit Camera Model CVPR 2023 Investigating a Generalization of Probabilistic Material Implication and Bayesian Conditionals UAI 2023