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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)

Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator ICML 2018 Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk COLT 2018 Globally Optimal Inlier Set Maximization for Atlanta Frame Estimation CVPR 2018 Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search IJCAI 2018 Global Non-convex Optimization with Discretized Diffusions NeurIPS 2018 Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation CoRL 2018 Global SNR Estimation of Speech Signals Using Entropy and Uncertainty Estimates from Dropout Networks Interspeech 2018 Global Versus Localized Generative Adversarial Nets CVPR 2018 G-NET: Effective GPU Sharing in NFV Systems NSDI 2018 Goal-Based Collective Decisions: Axiomatics and Computational Complexity IJCAI 2018 Goodness-of-Fit Testing for Discrete Distributions via Stein Discrepancy ICML 2018 Goodness-of-Fit Tests for Random Partitions via Symmetric Polynomials JMLR 2018 GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration NeurIPS 2018 Gradient Coding from Cyclic MDS Codes and Expander Graphs ICML 2018 Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers ICML 2018 Gradient Descent for Spiking Neural Networks NeurIPS 2018 Gradient Descent Learns Linear Dynamical Systems JMLR 2018 Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima ICML 2018 Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation NeurIPS 2018 Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks ICML 2018 Gradient Diversity: a Key Ingredient for Scalable Distributed Learning AISTATS 2018 Gradient Estimators for Implicit Models ICLR 2018 Gradient Layer: Enhancing the Convergence of Adversarial Training for Generative Models AISTATS 2018 Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks ICML 2018 Gradient Sparsification for Communication-Efficient Distributed Optimization NeurIPS 2018