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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)

Learning from Data with Heterogeneous Noise using SGD AISTATS 2015 Learning From Massive Noisy Labeled Data for Image Classification CVPR 2015 Learning From Small Samples: An Analysis of Simple Decision Heuristics NeurIPS 2015 Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring NeurIPS 2015 Learning Nonlinear Spectral Filters for Color Image Reconstruction ICCV 2015 Learning Parametric-Output HMMs with Two Aliased States ICML 2015 Learning Scale-Free Networks by Dynamic Node Specific Degree Prior ICML 2015 Learning Sparse Low-Threshold Linear Classifiers JMLR 2015 Learning spatiotemporal trajectories from manifold-valued longitudinal data NeurIPS 2015 Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels NeurIPS 2015 Learning structured densities via infinite dimensional exponential families NeurIPS 2015 Learning the dependence structure of rare events: a non-asymptotic study COLT 2015 Learning Theory and Algorithms for Forecasting Non-stationary Time Series NeurIPS 2015 Learning Theory of Randomized Kaczmarz Algorithm JMLR 2015 Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data JMLR 2015 Learning The Structure of Deep Convolutional Networks ICCV 2015 Learning to Divide and Conquer for Online Multi-Target Tracking ICCV 2015 Learning to Identify Concise Regular Expressions that Describe Email Campaigns JMLR 2015 Learning to Search Better than Your Teacher ICML 2015 Learning with a Wasserstein Loss NeurIPS 2015 Learning with Incremental Iterative Regularization NeurIPS 2015 Learning with Square Loss: Localization through Offset Rademacher Complexity COLT 2015 Learning with Symmetric Label Noise: The Importance of Being Unhinged NeurIPS 2015 Learning with the Maximum Correntropy Criterion Induced Losses for Regression JMLR 2015 Leave-One-Out Kernel Optimization for Shadow Detection ICCV 2015