conftrace_

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

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29

44

31

52

189

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352

382

457

787

816

828

1,066

1,391

1,913

3,012

3,646

4,303

4,450

5,357

6,155

5,854

1,521

2000

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Papers (including subtopics)

Learning and Inference via Maximum Inner Product Search ICML 2016 Learning and Testing Junta Distributions COLT 2016 Learning a Translation Model from Word Lattices Interspeech 2016 Learning Bound for Parameter Transfer Learning NeurIPS 2016 Learning brain regions via large-scale online structured sparse dictionary learning NeurIPS 2016 Learning Combinatorial Functions from Pairwise Comparisons COLT 2016 Learning Communities in the Presence of Errors COLT 2016 Learning Deep Embeddings with Histogram Loss NeurIPS 2016 Learning Deep Representation for Imbalanced Classification CVPR 2016 Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs NeurIPS 2016 Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers ACML 2016 Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices NeurIPS 2016 Learning Infinite RBMs with Frank-Wolfe NeurIPS 2016 Learning in Games: Robustness of Fast Convergence NeurIPS 2016 Learning Kernels with Random Features NeurIPS 2016 Learning Mixtures of Plackett-Luce Models ICML 2016 Learning Parameters of Hybrid Time Bayesian Networks PGM 2016 Learning Parametric Sparse Models for Image Super-Resolution NeurIPS 2016 Learning privately from multiparty data ICML 2016 Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization AISTATS 2016 Learning Simple Auctions COLT 2016 Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization ICML 2016 Learning Structured Sparsity in Deep Neural Networks NeurIPS 2016 Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods NeurIPS 2016 Learning Taxonomy Adaptation in Large-scale Classification JMLR 2016