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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)

Diminishing Returns Shape Constraints for Interpretability and Regularization NeurIPS 2018 Direct Learning to Rank And Rerank AISTATS 2018 Direct Runge-Kutta Discretization Achieves Acceleration NeurIPS 2018 Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification NeurIPS 2018 Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning ICML 2018 Discovering Phonesthemes with Sparse Regularization NAACL 2018 Discovering Point Lights With Intensity Distance Fields CVPR 2018 Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms ICML 2018 Discrete Factorization Machines for Fast Feature-based Recommendation IJCAI 2018 Discrete Interventions in Hawkes Processes with Applications in Invasive Species Management IJCAI 2018 Discretely Relaxing Continuous Variables for tractable Variational Inference NeurIPS 2018 Discrimination-aware Channel Pruning for Deep Neural Networks NeurIPS 2018 Discriminative Learning of Prediction Intervals AISTATS 2018 Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients ICML 2018 Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs ICML 2018 Distilled Wasserstein Learning for Word Embedding and Topic Modeling NeurIPS 2018 Distinguishing Question Subjectivity from Difficulty for Improved Crowdsourcing ACML 2018 Distractor Generation for Multiple Choice Questions Using Learning to Rank NAACL 2018 Distributable Consistent Multi-Object Matching CVPR 2018 Distributed k-Clustering for Data with Heavy Noise NeurIPS 2018 Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go? ICML 2018 Distributed Clustering via LSH Based Data Partitioning ICML 2018 Distributed Distributional Deterministic Policy Gradients ICLR 2018 Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization NeurIPS 2018 Distributed Multi-Player Bandits - a Game of Thrones Approach NeurIPS 2018