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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)

Stochastic first-order methods: non-asymptotic and computer-aided analyses via potential functions COLT 2019 Stochastic Frank-Wolfe for Composite Convex Minimization NeurIPS 2019 Stochastic Gradient Descent Learns State Equations with Nonlinear Activations COLT 2019 Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate AISTATS 2019 Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors AISTATS 2019 Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction NeurIPS 2019 Stochastic Gradient/Mirror Descent: Minimax Optimality and Implicit Regularization ICLR 2019 Stochastic Gradient Push for Distributed Deep Learning ICML 2019 Stochastic Gradient Trees ACML 2019 Stochastic Modified Equations and Dynamics of Stochastic Gradient Algorithms I: Mathematical Foundations JMLR 2019 Stochastic Negative Mining for Learning with Large Output Spaces AISTATS 2019 Stochastic Nonconvex Optimization with Large Minibatches ALT 2019 Stochastic Optimal Control as Approximate Input Inference CoRL 2019 Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence ICML 2019 Stochastic Optimization of Sorting Networks via Continuous Relaxations ICLR 2019 Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates NeurIPS 2019 Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond NeurIPS 2019 Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers NeurIPS 2019 Stochastic Submodular Maximization with Performance-Dependent Item Costs AAAI 2019 Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization AISTATS 2019 Stochastic Variance-Reduced Cubic Regularization Methods JMLR 2019 Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization NeurIPS 2019 Strategic Signaling for Selling Information Goods IJCAI 2019 Strategizing against No-regret Learners NeurIPS 2019 Strategy Logic with Simple Goals: Tractable Reasoning about Strategies IJCAI 2019