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Optimization

12,959 papers (55 classified directly here)

Subtopics

Continuous Optimization (3,907) Stochastic Methods (2,788) Combinatorial Optimization (2,409) Online Algorithms (2,192) Optimization (1,207) Game Theory (944) Discrete Optimization (705) Convex Optimization (591) Optimal Transport (369) Sparse Optimization (347) Optimal Control (225) Global Optimization (190) Distributed Learning (157) Multi-Objective Optimization (125) Approximation Algorithms (110) Bayesian Optimization (100) Distributed Optimization (99) Theory (98) Non-Convex Optimization (76) Kernel Methods (73) Compressed Sensing (72) Robust Optimization (70) Evolutionary Algorithm (58) Linear Programming (43) Algorithms (10) Algorithm (10) Integer Programming (9) Submodular Optimization (9) Matrix Completion (8) Bilevel Optimization (8) Tensor Decomposition (6) Regularization (5)

Papers per year

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Papers (including subtopics)

Training a Tucker Model With Shared Factors: a Riemannian Optimization Approach AISTATS 2024
Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression AISTATS 2024
Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence AISTATS 2024
Cylindrical Thompson Sampling for High-Dimensional Bayesian Optimization AISTATS 2024
Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks AISTATS 2024
Sequential learning of the Pareto front for multi-objective bandits AISTATS 2024
Equivalence Testing: The Power of Bounded Adaptivity AISTATS 2024
On the estimation of persistence intensity functions and linear representations of persistence diagrams AISTATS 2024
Dissimilarity Bandits AISTATS 2024
Consistent Optimal Transport with Empirical Conditional Measures AISTATS 2024
Stochastic Extragradient with Random Reshuffling: Improved Convergence for Variational Inequalities AISTATS 2024
On the Nyström Approximation for Preconditioning in Kernel Machines AISTATS 2024
Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent AISTATS 2024
LP-based Construction of DC Decompositions for Efficient Inference of Markov Random Fields AISTATS 2024
The sample complexity of ERMs in stochastic convex optimization AISTATS 2024
Fitting ARMA Time Series Models without Identification: A Proximal Approach AISTATS 2024
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport AISTATS 2024
Multi-objective Optimization via Wasserstein-Fisher-Rao Gradient Flow AISTATS 2024
A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent AISTATS 2024
Near-Optimal Policy Optimization for Correlated Equilibrium in General-Sum Markov Games AISTATS 2024
Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels AISTATS 2024
Contextual Bandits with Budgeted Information Reveal AISTATS 2024
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization AISTATS 2024
Informative Path Planning with Limited Adaptivity AISTATS 2024
Acceleration and Implicit Regularization in Gaussian Phase Retrieval AISTATS 2024