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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)

SSN_MLRG1 at SemEval-2017 Task 4: Sentiment Analysis in Twitter Using Multi-Kernel Gaussian Process Classifier SemEval 2017 SSN_MLRG1 at SemEval-2017 Task 5: Fine-Grained Sentiment Analysis Using Multiple Kernel Gaussian Process Regression Model SemEval 2017 Stability of Controllers for Gaussian Process Dynamics JMLR 2017 Stabilized Sparse Online Learning for Sparse Data JMLR 2017 Stabilizing Training of Generative Adversarial Networks through Regularization NeurIPS 2017 Statistical and Computational Guarantees for the Baum-Welch Algorithm JMLR 2017 Statistical Cost Sharing NeurIPS 2017 Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening ICML 2017 Statistical Inference with Unnormalized Discrete Models and Localized Homogeneous Divergences JMLR 2017 Stein Variational Gradient Descent as Gradient Flow NeurIPS 2017 Stereo-Based 3D Reconstruction of Dynamic Fluid Surfaces by Global Optimization CVPR 2017 Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference NeurIPS 2017 StingyCD: Safely Avoiding Wasteful Updates in Coordinate Descent ICML 2017 Stochastic Adaptive Quasi-Newton Methods for Minimizing Expected Values ICML 2017 Stochastic and Adversarial Online Learning without Hyperparameters NeurIPS 2017 Stochastic Approximation for Canonical Correlation Analysis NeurIPS 2017 Stochastic Bouncy Particle Sampler ICML 2017 Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$ COLT 2017 Stochastic Constraint Programming with And-Or Branch-and-Bound IJCAI 2017 Stochastic Convex Optimization: Faster Local Growth Implies Faster Global Convergence ICML 2017 Stochastic DCA for the Large-sum of Non-convex Functions Problem and its Application to Group Variable Selection in Classification ICML 2017 Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines AISTATS 2017 Stochastic Gradient Descent as Approximate Bayesian Inference JMLR 2017 Stochastic Gradient MCMC Methods for Hidden Markov Models ICML 2017 Stochastic Gradient Monomial Gamma Sampler ICML 2017