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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)

Proximal Methods for Hierarchical Sparse Coding JMLR 2011 Pylon Model for Semantic Segmentation NeurIPS 2011 Quadratic Weighted Automata:Spectral Algorithm and Likelihood Maximization ACML 2011 Quasi-Newton Methods for Markov Chain Monte Carlo NeurIPS 2011 Query-Aware MCMC NeurIPS 2011 Real-time prioritized kinematic control under inequality constraints for redundant manipulators RSS 2011 Reconstructing Patterns of Information Diffusion from Incomplete Observations NeurIPS 2011 Regret Bounds for the Adaptive Control of Linear Quadratic Systems COLT 2011 Relational Learning with One Network: An Asymptotic Analysis AISTATS 2011 Relative Density-Ratio Estimation for Robust Distribution Comparison NeurIPS 2011 Revisiting MAP Estimation, Message Passing and Perfect Graphs AISTATS 2011 Robust approachability and regret minimization in games with partial monitoring COLT 2011 Robust Bayesian Matrix Factorisation AISTATS 2011 Robust Lasso with missing and grossly corrupted observations NeurIPS 2011 RTRMC: A Riemannian trust-region method for low-rank matrix completion NeurIPS 2011 Safe Learning: bridging the gap between Bayes, MDL and statistical learning theory via empirical convexity COLT 2011 Sample Complexity Bounds for Differentially Private Learning COLT 2011 Scalable Training of Mixture Models via Coresets NeurIPS 2011 Select and Sample - A Model of Efficient Neural Inference and Learning NeurIPS 2011 Selecting the State-Representation in Reinforcement Learning NeurIPS 2011 Semi-supervised Learning by Higher Order Regularization AISTATS 2011 Semi-Supervised Learning with Measure Propagation JMLR 2011 Semi-supervised Regression via Parallel Field Regularization NeurIPS 2011 Sequence learning with hidden units in spiking neural networks NeurIPS 2011 Sequential Event Prediction with Association Rules COLT 2011