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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

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

Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits NeurIPS 2020 Statistical Estimation of the Poincaré constant and Application to Sampling Multimodal Distributions AISTATS 2020 Statistical Guarantees of Distributed Nearest Neighbor Classification NeurIPS 2020 Statistical Learning with a Nuisance Component (Extended Abstract) IJCAI 2020 Statistically Efficient Off-Policy Policy Gradients ICML 2020 Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization ICML 2020 Statistical Parsing of Tree Wrapping Grammars COLING 2020 Statistical Power and Translationese in Machine Translation Evaluation EMNLP 2020 Statistical-Query Lower Bounds via Functional Gradients NeurIPS 2020 Staying True to Your Word: (How) Can Attention Become Explanation? ACL 2020 Steady State Analysis of Episodic Reinforcement Learning NeurIPS 2020 Steady-State Policy Synthesis in Multichain Markov Decision Processes IJCAI 2020 STEER : Simple Temporal Regularization For Neural ODE NeurIPS 2020 Stein Self-Repulsive Dynamics: Benefits From Past Samples NeurIPS 2020 Stein Variational Inference for Discrete Distributions AISTATS 2020 Stein Variational Model Predictive Control CoRL 2020 Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning AISTATS 2020 Stochastically Dominant Distributional Reinforcement Learning ICML 2020 Stochastic Approximate Gradient Descent via the Langevin Algorithm AAAI 2020 Stochastic AUC Maximization with Deep Neural Networks ICLR 2020 Stochastic Bandits with Delay-Dependent Payoffs AISTATS 2020 Stochastic Classifiers for Unsupervised Domain Adaptation CVPR 2020 Stochastic Conditional Gradient Methods: From Convex Minimization to Submodular Maximization JMLR 2020 Stochastic Deep Gaussian Processes over Graphs NeurIPS 2020 Stochastic Differential Equations with Variational Wishart Diffusions ICML 2020