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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

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52
352
828
3,646
5,854
2000
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Papers (including subtopics)

Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously ICML 2019 Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits UAI 2019 Belief Change and Non-Monotonic Reasoning Sans Compactness AAAI 2019 Belief Propagation: Accurate Marginals or Accurate Partition Function – Where is the Difference? UAI 2019 Benchmarking Approximate Inference Methods for Neural Structured Prediction NAACL 2019 Benchmarking Benchmarks: Introducing New Automatic Indicators for Benchmarking Spoken Language Understanding Corpora Interspeech 2019 Benchmarking Hierarchical Script Knowledge NAACL 2019 Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA ICML 2019 Bernoulli Race Particle Filters AISTATS 2019 Best Answers over Incomplete Data : Complexity and First-Order Rewritings IJCAI 2019 Best Arm Identification for Contaminated Bandits JMLR 2019 Best of many worlds: Robust model selection for online supervised learning AISTATS 2019 Better Exploiting Latent Variables in Text Modeling ACL 2019 Better generalization with less data using robust gradient descent ICML 2019 Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms NeurIPS 2019 Beyond Backprop: Online Alternating Minimization with Auxiliary Variables ICML 2019 Beyond BLEU: Training Neural Machine Translation with Semantic Similarity ACL 2019 Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs NeurIPS 2019 Beyond Gradient Descent for Regularized Segmentation Losses CVPR 2019 Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance COLT 2019 Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization NeurIPS 2019 Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration NeurIPS 2019 Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior ICML 2019 Beyond the Single Neuron Convex Barrier for Neural Network Certification NeurIPS 2019 Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting NeurIPS 2019