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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)

Optimal Arms Identification with Knapsacks ICML 2023 Optimal Block-wise Asymmetric Graph Construction for Graph-based Semi-supervised Learning NeurIPS 2023 Optimal Budget Allocation for Crowdsourcing Labels for Graphs UAI 2023 Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian ICLR 2023 Optimal Contextual Bandits with Knapsacks under Realizability via Regression Oracles AISTATS 2023 Optimal control of speech with context-dependent articulatory targets Interspeech 2023 Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes NeurIPS 2023 Optimal Convergence Rates for Agnostic Nyström Kernel Learning ICML 2023 Optimal Convergence Rates for Distributed Nystroem Approximation JMLR 2023 Optimal cross-learning for contextual bandits with unknown context distributions NeurIPS 2023 Optimal Excess Risk Bounds for Empirical Risk Minimization on p-Norm Linear Regression NeurIPS 2023 Optimal Execution via Multi-Objective Multi-Armed Bandits (Student Abstract) AAAI 2023 Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure NeurIPS 2023 Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization NeurIPS 2023 Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs ICML 2023 Optimality in Mean Estimation: Beyond Worst-Case, Beyond Sub-Gaussian, and Beyond 1+α Moments NeurIPS 2023 Optimality of Message-Passing Architectures for Sparse Graphs NeurIPS 2023 Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits ICML 2023 Optimal Learners for Realizable Regression: PAC Learning and Online Learning NeurIPS 2023 Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference ICML 2023 Optimal Nonlinearities Improve Generalization Performance of Random Features ACML 2023 Optimal No-Regret Learning for One-Sided Lipschitz Functions ICML 2023 Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits ICML 2023 Optimal Parameter-Transfer Learning by Semiparametric Model Averaging JMLR 2023 Optimal Preconditioning and Fisher Adaptive Langevin Sampling NeurIPS 2023