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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)

Regularized Graph Matching for Correspondence Identification under Uncertainty in Collaborative Perception RSS 2020 Regularized linear autoencoders recover the principal components, eventually NeurIPS 2020 Regularized Optimal Transport is Ground Cost Adversarial ICML 2020 Regularized Wasserstein Means for Aligning Distributional Data AAAI 2020 Regularizing activations in neural networks via distribution matching with the Wasserstein metric ICLR 2020 Regularizing Class-Wise Predictions via Self-Knowledge Distillation CVPR 2020 Regularizing CNN Transfer Learning With Randomised Regression CVPR 2020 Regularizing Neural Networks via Minimizing Hyperspherical Energy CVPR 2020 Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs ICLR 2020 Reinforcement Learning for Integer Programming: Learning to Cut ICML 2020 Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism ICML 2020 Reinforcement Learning in Continuous Time and Space: A Stochastic Control Approach JMLR 2020 Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting NeurIPS 2020 Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound ICML 2020 Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes AAAI 2020 Reinforcement Learning When All Actions Are Not Always Available AAAI 2020 Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension NeurIPS 2020 Reinforcement Learning with Perturbed Rewards AAAI 2020 Reinforcement Mechanism Design: With Applications to Dynamic Pricing in Sponsored Search Auctions AAAI 2020 Reinforcing Neural Network Stability with Attractor Dynamics AAAI 2020 Relative gradient optimization of the Jacobian term in unsupervised deep learning NeurIPS 2020 Relative Interior Rule in Block-Coordinate Descent CVPR 2020 Reliable Fidelity and Diversity Metrics for Generative Models ICML 2020 Reliable Graph Neural Networks via Robust Aggregation NeurIPS 2020 Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation CVPR 2020