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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)

Bayesian estimation of orientation preference maps NeurIPS 2009 Bayesian Nonparametric Models on Decomposable Graphs NeurIPS 2009 Bayesian Source Localization with the Multivariate Laplace Prior NeurIPS 2009 Bayesian Sparse Factor Models and DAGs Inference and Comparison NeurIPS 2009 Beyond Convexity: Online Submodular Minimization NeurIPS 2009 Bi-Level Path Following for Cross Validated Solution of Kernel Quantile Regression JMLR 2009 Bootstrapping from Game Tree Search NeurIPS 2009 Bounded Kernel-Based Online Learning JMLR 2009 Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition NeurIPS 2009 Canonical Time Warping for Alignment of Human Behavior NeurIPS 2009 Classification with Gaussians and Convex Loss JMLR 2009 Clustering sequence sets for motif discovery NeurIPS 2009 Code-specific policy gradient rules for spiking neurons NeurIPS 2009 Complexity of Decentralized Control: Special Cases NeurIPS 2009 Compressed Least-Squares Regression NeurIPS 2009 Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors JMLR 2009 Consistency and Localizability JMLR 2009 Construction of Nonparametric Bayesian Models from Parametric Bayes Equations NeurIPS 2009 Controlling the False Discovery Rate of the Association/Causality Structure Learned with the PC Algorithm JMLR 2009 Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation NeurIPS 2009 Convex Relaxation of Mixture Regression with Efficient Algorithms NeurIPS 2009 Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies NeurIPS 2009 Data-driven calibration of linear estimators with minimal penalties NeurIPS 2009 Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process NeurIPS 2009 Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods JMLR 2009