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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)

Online Learning for Active Cache Synchronization ICML 2020 Online Learning in Changing Environments IJCAI 2020 Online Learning Using Only Peer Prediction AISTATS 2020 Online Learning with Continuous Variations: Dynamic Regret and Reductions AISTATS 2020 Online Learning with Dependent Stochastic Feedback Graphs ICML 2020 Online learning with dynamics: A minimax perspective NeurIPS 2020 Online Learning with Imperfect Hints ICML 2020 Online Learning with Primary and Secondary Losses NeurIPS 2020 Online Learning with Vector Costs and Bandits with Knapsacks COLT 2020 Online Linear Optimization with Many Hints NeurIPS 2020 Online matrix factorization for Markovian data and applications to Network Dictionary Learning JMLR 2020 Online Multi-Kernel Learning with Graph-Structured Feedback ICML 2020 Online Multitask Learning with Long-Term Memory NeurIPS 2020 Online Neural Connectivity Estimation with Noisy Group Testing NeurIPS 2020 Online Non-Convex Optimization with Imperfect Feedback NeurIPS 2020 Online Parameter-Free Learning of Multiple Low Variance Tasks UAI 2020 Online Planning with Lookahead Policies NeurIPS 2020 Online Pricing with Offline Data: Phase Transition and Inverse Square Law ICML 2020 Online Revenue Maximization for Server Pricing IJCAI 2020 Online Robust Regression via SGD on the l1 loss NeurIPS 2020 On Log-Loss Scores and (No) Privacy EMNLP 2020 On Long-Tailed Phenomena in Neural Machine Translation EMNLP 2020 On Losses for Modern Language Models EMNLP 2020 On Minimax Optimality of GANs for Robust Mean Estimation AISTATS 2020 On Numerosity of Deep Neural Networks NeurIPS 2020