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

Learning rule influences recurrent network representations but not attractor structure in decision-making tasks NeurIPS 2021 Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases AAAI 2021 Learning Smooth and Fair Representations AISTATS 2021 Learning Space Partitions for Path Planning NeurIPS 2021 Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives JMLR 2021 Learning sparse mixtures of permutations from noisy information COLT 2021 Learning Spatially-Variant MAP Models for Non-Blind Image Deblurring CVPR 2021 Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory L4DC 2021 Learning Stochastic Behaviour from Aggregate Data ICML 2021 Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound NeurIPS 2021 Learning Strategies in Decentralized Matching Markets under Uncertain Preferences JMLR 2021 Learning Task Sampling Policy for Multitask Learning EMNLP 2021 Learning Temporal Point Processes with Intermittent Observations AISTATS 2021 Learning the Non-Differentiable Optimization for Blind Super-Resolution CVPR 2021 Learning the optimal Tikhonov regularizer for inverse problems NeurIPS 2021 Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks NeurIPS 2021 Learning the Parameters of Bayesian Networks from Uncertain Data AAAI 2021 Learning to Ask Conversational Questions by Optimizing Levenshtein Distance ACL 2021 Learning To Better Segment Objects From Unseen Classes With Unlabeled Videos ICCV 2021 Learning to Count via Unbalanced Optimal Transport AAAI 2021 Learning to Defend by Learning to Attack AISTATS 2021 Learning to delegate for large-scale vehicle routing NeurIPS 2021 Learning to Generate 3D Shapes with Generative Cellular Automata ICLR 2021 Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer NeurIPS 2021 Learning-to-learn non-convex piecewise-Lipschitz functions NeurIPS 2021