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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)

Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance JMLR 2010 Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks NeurIPS 2010 Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains NeurIPS 2010 Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models JMLR 2010 Joint Analysis of Time-Evolving Binary Matrices and Associated Documents NeurIPS 2010 Kernel Partial Least Squares is Universally Consistent AISTATS 2010 Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development NeurIPS 2010 Learnability, Stability and Uniform Convergence JMLR 2010 Learning Bayesian Network Structure using LP Relaxations AISTATS 2010 Learning Bounds for Importance Weighting NeurIPS 2010 Learning concept graphs from text with stick-breaking priors NeurIPS 2010 Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity AISTATS 2010 Learning Instance-Specific Predictive Models JMLR 2010 Learning invariant features using the Transformed Indian Buffet Process NeurIPS 2010 Learning Kernels with Radiuses of Minimum Enclosing Balls NeurIPS 2010 Learning Multiple Tasks using Manifold Regularization NeurIPS 2010 Learning Multiple Tasks with a Sparse Matrix-Normal Penalty NeurIPS 2010 Learning Networks of Stochastic Differential Equations NeurIPS 2010 Learning Nonlinear Dynamic Models from Non-sequenced Data AISTATS 2010 Learning Polyhedral Classifiers Using Logistic Function ACML 2010 Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors NeurIPS 2010 Learning the Structure of Deep Sparse Graphical Models AISTATS 2010 Learning Translation Invariant Kernels for Classification JMLR 2010 Learning via Gaussian Herding NeurIPS 2010 Learning with Blocks: Composite Likelihood and Contrastive Divergence AISTATS 2010