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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)

Understanding the difficulty of training deep feedforward neural networks AISTATS 2010 Universal Consistency of Multi-Class Support Vector Classification NeurIPS 2010 Universal Kernels on Non-Standard Input Spaces NeurIPS 2010 Using Contextual Representations to Efficiently Learn Context-Free Languages JMLR 2010 Variable margin losses for classifier design NeurIPS 2010 Variational bounds for mixed-data factor analysis NeurIPS 2010 Variational Inference over Combinatorial Spaces NeurIPS 2010 Variational methods for Reinforcement Learning AISTATS 2010 Variational Relevance Vector Machine for Tabular Data ACML 2010 Why are DBNs sparse? AISTATS 2010 Why are some word orders more common than others? A uniform information density account NeurIPS 2010 Why Does Unsupervised Pre-training Help Deep Learning? AISTATS 2010 Why Does Unsupervised Pre-training Help Deep Learning? JMLR 2010 Worst-case bounds on the quality of max-product fixed-points NeurIPS 2010 Worst-Case Linear Discriminant Analysis NeurIPS 2010 L_1-Penalized Robust Estimation for a Class of Inverse Problems Arising in Multiview Geometry NeurIPS 2009 A Bayesian Analysis of Dynamics in Free Recall NeurIPS 2009 A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation NeurIPS 2009 Accelerated Gradient Methods for Stochastic Optimization and Online Learning NeurIPS 2009 Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models NeurIPS 2009 Adaptive Design Optimization in Experiments with People NeurIPS 2009 Adaptive False Discovery Rate Control under Independence and Dependence JMLR 2009 Adaptive Regularization for Transductive Support Vector Machine NeurIPS 2009 Adaptive Regularization of Weight Vectors NeurIPS 2009 A Data-Driven Approach to Modeling Choice NeurIPS 2009