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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
352
828
3,646
5,854
2000
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Papers (including subtopics)

Classification Methods with Reject Option Based on Convex Risk Minimization JMLR 2010 Classification with Incomplete Data Using Dirichlet Process Priors JMLR 2010 Closing the Learning-Planning Loop with Predictive State Representations RSS 2010 Coherent Inference on Optimal Play in Game Trees AISTATS 2010 Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm NeurIPS 2010 Collaborative Filtering via Rating Concentration AISTATS 2010 Composite Binary Losses JMLR 2010 Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning NeurIPS 2010 Consensus-Based Distributed Support Vector Machines JMLR 2010 Consistent Nonparametric Tests of Independence JMLR 2010 Construction of Dependent Dirichlet Processes based on Poisson Processes NeurIPS 2010 Content-based Image Retrieval with Multinomial Relevance Feedback ACML 2010 Contextual Multi-Armed Bandits AISTATS 2010 Convexity of Proper Composite Binary Losses AISTATS 2010 Convex Multiple-Instance Learning by Estimating Likelihood Ratio NeurIPS 2010 Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials AISTATS 2010 Copula Processes NeurIPS 2010 CUR from a Sparse Optimization Viewpoint NeurIPS 2010 Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication NeurIPS 2010 Decomposing Isotonic Regression for Efficiently Solving Large Problems NeurIPS 2010 Dense Message Passing for Sparse Principal Component Analysis AISTATS 2010 Dependent Indian Buffet Processes AISTATS 2010 Descent Methods for Tuning Parameter Refinement AISTATS 2010 Deterministic Bayesian inference for the $p*$ model AISTATS 2010 Deterministic Single-Pass Algorithm for LDA NeurIPS 2010