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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)

Robust Multi-objective Learning with Mentor Feedback COLT 2014 Robust Policies via Meshing for Metastable Rough Terrain Walking RSS 2014 Robust Principal Component Analysis with Complex Noise ICML 2014 Robust RegBayes: Selectively Incorporating First-Order Logic Domain Knowledge into Bayesian Models ICML 2014 Robust Separation of Reflection from Multiple Images CVPR 2014 Robust Stochastic Principal Component Analysis AISTATS 2014 Robust Tensor Decomposition with Gross Corruption NeurIPS 2014 Rounding-based Moves for Metric Labeling NeurIPS 2014 Safe Screening with Variational Inequalities and Its Application to Lasso ICML 2014 SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives NeurIPS 2014 Saliency Optimization from Robust Background Detection CVPR 2014 Sample-based approximate regularization ICML 2014 Sample Complexity Bounds on Differentially Private Learning via Communication Complexity COLT 2014 Sample Compression for Multi-label Concept Classes COLT 2014 Sample Efficient Reinforcement Learning with Gaussian Processes ICML 2014 Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature NeurIPS 2014 Scalable and Robust Bayesian Inference via the Median Posterior ICML 2014 Scalable Collaborative Bayesian Preference Learning AISTATS 2014 Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications ICML 2014 Scalable Inference for Neuronal Connectivity from Calcium Imaging NeurIPS 2014 Scalable Kernel Methods via Doubly Stochastic Gradients NeurIPS 2014 Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices NeurIPS 2014 Scalable Non-linear Learning with Adaptive Polynomial Expansions NeurIPS 2014 Scalable Semidefinite Relaxation for Maximum A Posterior Estimation ICML 2014 Scalable Variational Bayesian Matrix Factorization with Side Information AISTATS 2014