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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)

Sub-exponential time Sum-of-Squares lower bounds for Principal Components Analysis NeurIPS 2022 Sublinear time algorithms for greedy selection in high dimensions UAI 2022 Subspace Adversarial Training CVPR 2022 Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap NeurIPS 2022 Subspace Learning for Effective Meta-Learning ICML 2022 Subspace Recovery from Heterogeneous Data with Non-isotropic Noise NeurIPS 2022 Sufficient reductions in regression with mixed predictors JMLR 2022 SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search ICLR 2022 Sum of Ranked Range Loss for Supervised Learning JMLR 2022 SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers COLING 2022 Super-Acceleration with Cyclical Step-sizes AISTATS 2022 Superposing many tickets into one: A performance booster for sparse neural network training UAI 2022 Supervised Learning with General Risk Functionals ICML 2022 Supervised Training of Conditional Monge Maps NeurIPS 2022 Supervising the Multi-Fidelity Race of Hyperparameter Configurations NeurIPS 2022 Supported Policy Optimization for Offline Reinforcement Learning NeurIPS 2022 Support Recovery in Sparse PCA with Incomplete Data NeurIPS 2022 Surprise Minimizing Multi-Agent Learning with Energy-based Models NeurIPS 2022 Surprising Instabilities in Training Deep Networks and a Theoretical Analysis NeurIPS 2022 Surrogate Gap Minimization Improves Sharpness-Aware Training ICLR 2022 Surrogate Likelihoods for Variational Annealed Importance Sampling ICML 2022 Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks ICLR 2022 Survey on Efficient Training of Large Neural Networks IJCAI 2022 Survival regression with proper scoring rules and monotonic neural networks AISTATS 2022 SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction MLHC 2022