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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)

Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms NeurIPS 2023 Sample Complexity of Distinguishing Cause from Effect AISTATS 2023 Sample Complexity of Forecast Aggregation NeurIPS 2023 Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning NeurIPS 2023 Sample Complexity of Kernel-Based Q-Learning AISTATS 2023 Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks ICLR 2023 Sample Complexity of Probability Divergences under Group Symmetry ICML 2023 Sample-Conditioned Hypothesis Stability Sharpens Information-Theoretic Generalization Bounds NeurIPS 2023 Sample-Dependent Adaptive Temperature Scaling for Improved Calibration AAAI 2023 Sample Efficiency of Data Augmentation Consistency Regularization AISTATS 2023 Sample-efficient Multi-objective Molecular Optimization with GFlowNets NeurIPS 2023 Samples With Low Loss Curvature Improve Data Efficiency CVPR 2023 Sampling-Based Accuracy Testing of Posterior Estimators for General Inference ICML 2023 Sampling-based inference for large linear models, with application to linearised Laplace ICLR 2023 Sampling From a Schrödinger Bridge AISTATS 2023 Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent NeurIPS 2023 Sampling from Structured Log-Concave Distributions via a Soft-Threshold Dikin Walk NeurIPS 2023 Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions ICLR 2023 Sampling random graph homomorphisms and applications to network data analysis JMLR 2023 Sampling weights of deep neural networks NeurIPS 2023 Sampling with Mollified Interaction Energy Descent ICLR 2023 Samsung Research China - Beijing at SemEval-2023 Task 2: An AL-R Model for Multilingual Complex Named Entity Recognition SemEval 2023 Sancus: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks (Extended Abstract) IJCAI 2023 SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models NeurIPS 2023 SAT-Based PAC Learning of Description Logic Concepts IJCAI 2023