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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)

Learning Mixtures of Smooth Product Distributions: Identifiability and Algorithm AISTATS 2019 Learning Models of Sequential Decision-Making with Partial Specification of Agent Behavior AAAI 2019 Learning Multi-Objective Rewards and User Utility Function in Contextual Bandits for Personalized Ranking IJCAI 2019 Learning Neural Networks with Adaptive Regularization NeurIPS 2019 Learning Neural Networks with Two Nonlinear Layers in Polynomial Time COLT 2019 Learning Neural PDE Solvers with Convergence Guarantees ICLR 2019 Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks NeurIPS 2019 Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model NeurIPS 2019 Learning Nonsymmetric Determinantal Point Processes NeurIPS 2019 Learning One-hidden-layer Neural Networks under General Input Distributions AISTATS 2019 Learning One-hidden-layer ReLU Networks via Gradient Descent AISTATS 2019 Learning Optimal Classification Trees Using a Binary Linear Program Formulation AAAI 2019 Learning Optimal Linear Regularizers ICML 2019 Learning Optimal Strategies to Commit To AAAI 2019 Learning Optimized Risk Scores JMLR 2019 Learning Parametric Constraints in High Dimensions from Demonstrations CoRL 2019 Learning Plackett-Luce Mixtures from Partial Preferences AAAI 2019 Learning Positive Functions with Pseudo Mirror Descent NeurIPS 2019 Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure ICLR 2019 Learning Protein Structure with a Differentiable Simulator ICLR 2019 Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization ACL 2019 Learning Resource Allocation and Pricing for Cloud Profit Maximization AAAI 2019 Learning Reward Machines for Partially Observable Reinforcement Learning NeurIPS 2019 Learning Robust Distance Metric with Side Information via Ratio Minimization of Orthogonally Constrained L21-Norm Distances IJCAI 2019 Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning NeurIPS 2019