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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)

LEARNED STEP SIZE QUANTIZATION ICLR 2020 Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning ACML 2020 Learning Adaptive Segmentation Policy for Simultaneous Translation EMNLP 2020 Learning Adversarial Markov Decision Processes with Bandit Feedback and Unknown Transition ICML 2020 Learning Architectures from an Extended Search Space for Language Modeling ACL 2020 Learning a Reinforced Agent for Flexible Exposure Bracketing Selection CVPR 2020 Learning a Single Neuron with Gradient Methods COLT 2020 Learning Augmentation Network via Influence Functions CVPR 2020 Learning-Augmented Data Stream Algorithms ICLR 2020 Learning Augmented Energy Minimization via Speed Scaling NeurIPS 2020 Learning a Unified Sample Weighting Network for Object Detection CVPR 2020 Learning Bayesian Networks Under Sparsity Constraints: A Parameterized Complexity Analysis IJCAI 2020 Learning Behaviors with Uncertain Human Feedback UAI 2020 Learning Bounds for Risk-sensitive Learning NeurIPS 2020 Learning by Minimizing the Sum of Ranked Range NeurIPS 2020 Learning by Repetition: Stochastic Multi-armed Bandits under Priming Effect UAI 2020 Learning Causal Effects via Weighted Empirical Risk Minimization NeurIPS 2020 Learning Certified Control Using Contraction Metric CoRL 2020 Learning CNF Blocking for Large-scale Author Name Disambiguation EMNLP 2020 Learning Combinatorial Solver for Graph Matching CVPR 2020 Learning Composable Energy Surrogates for PDE Order Reduction NeurIPS 2020 Learning compositional functions via multiplicative weight updates NeurIPS 2020 Learning Constraints for Structured Prediction Using Rectifier Networks ACL 2020 Learning Data-adaptive Non-parametric Kernels JMLR 2020 Learning decomposable models by coarsening PGM 2020