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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)

An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge AISTATS 2023 An Ordinal Latent Variable Model of Conflict Intensity ACL 2023 A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective NeurIPS 2023 A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces AISTATS 2023 An SDE for Modeling SAM: Theory and Insights ICML 2023 Answering Complex Logical Queries on Knowledge Graphs via Query Computation Tree Optimization ICML 2023 Anticipatory Fictitious Play IJCAI 2023 Anti-Symmetric DGN: a stable architecture for Deep Graph Networks ICLR 2023 Anytime-Competitive Reinforcement Learning with Policy Prior NeurIPS 2023 Anytime Model Selection in Linear Bandits NeurIPS 2023 Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods AAAI 2023 A one-sample decentralized proximal algorithm for non-convex stochastic composite optimization UAI 2023 A Parameter-Free Conditional Gradient Method for Composite Minimization under Hölder Condition JMLR 2023 A Parameterized Theory of PAC Learning AAAI 2023 A Partially Observable Monte Carlo Planning Algorithm Based on Path Modification ACML 2023 A Path to Simpler Models Starts With Noise NeurIPS 2023 A PDE approach for regret bounds under partial monitoring JMLR 2023 A Permutation-Free Kernel Independence Test JMLR 2023 A Picture of the Space of Typical Learnable Tasks ICML 2023 A policy gradient approach for optimization of smooth risk measures UAI 2023 Approval-Based Voting with Mixed Goods AAAI 2023 Approximate Causal Effect Identification under Weak Confounding ICML 2023 Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent NeurIPS 2023 Approximate inference of marginals using the IBIA framework NeurIPS 2023 Approximately Bayes-optimal pseudo-label selection UAI 2023