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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
352
828
3,646
5,854
2000
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Papers (including subtopics)

A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems NeurIPS 2020 Asking the Right Questions to the Right Users: Active Learning with Imperfect Oracles AAAI 2020 A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty RSS 2020 A Sparse Semismooth Newton Based Proximal Majorization-Minimization Algorithm for Nonconvex Square-Root-Loss Regression Problems JMLR 2020 A Spectral Energy Distance for Parallel Speech Synthesis NeurIPS 2020 A Spectral Method for Unsupervised Multi-Document Summarization EMNLP 2020 Assessing Local Generalization Capability in Deep Models AISTATS 2020 Assise: Performance and Availability via Client-local NVM in a Distributed File System OSDI 2020 Assisted Learning: A Framework for Multi-Organization Learning NeurIPS 2020 Associative Memory in Iterated Overparameterized Sigmoid Autoencoders ICML 2020 A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning ACML 2020 A Statistical Framework for Low-bitwidth Training of Deep Neural Networks NeurIPS 2020 A Statistical Learning Approach to Modal Regression JMLR 2020 A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning NeurIPS 2020 A Stein Goodness-of-fit Test for Directional Distributions AISTATS 2020 Asteroid: The PyTorch-Based Audio Source Separation Toolkit for Researchers Interspeech 2020 A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control AAAI 2020 A Stochastic Derivative Free Optimization Method with Momentum ICLR 2020 A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm NeurIPS 2020 A Study on Encodings for Neural Architecture Search NeurIPS 2020 A SUPER* Algorithm to Optimize Paper Bidding in Peer Review UAI 2020 A Swiss Army Knife for Minimax Optimal Transport ICML 2020 Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning AISTATS 2020 Asymptotically Optimal Exact Minibatch Metropolis-Hastings NeurIPS 2020 Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms AISTATS 2020