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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)

Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation NeurIPS 2021 Reward is enough for convex MDPs NeurIPS 2021 RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation ACL 2021 Reweighting Augmented Samples by Minimizing the Maximal Expected Loss ICLR 2021 Rewriter-Evaluator Architecture for Neural Machine Translation ACL 2021 RGB-D Saliency Detection via Cascaded Mutual Information Minimization ICCV 2021 Rich Prosody Diversity Modelling with Phone-Level Mixture Density Network Interspeech 2021 Ridge Regression with Over-parametrized Two-Layer Networks Converge to Ridgelet Spectrum AISTATS 2021 Riemannian Stochastic Recursive Momentum Method for non-Convex Optimization IJCAI 2021 Riggable 3D Face Reconstruction via In-Network Optimization CVPR 2021 Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration AISTATS 2021 Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions AAAI 2021 Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks ICLR 2021 Risk-Averse Bayes-Adaptive Reinforcement Learning NeurIPS 2021 Risk-averse Heteroscedastic Bayesian Optimization NeurIPS 2021 Risk-Averse Zero-Order Trajectory Optimization CoRL 2021 Risk-Aware Transfer in Reinforcement Learning using Successor Features NeurIPS 2021 Risk Bounds and Calibration for a Smart Predict-then-Optimize Method NeurIPS 2021 Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning ICML 2021 Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures NeurIPS 2021 Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs JMLR 2021 Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning NeurIPS 2021 Risk Monotonicity in Statistical Learning NeurIPS 2021 Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach ICML 2021 Rissanen Data Analysis: Examining Dataset Characteristics via Description Length ICML 2021