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stochastic optimization

1060 papers

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Also known as

SO SVRG SSO SGD

Co-occurring keywords

regret bound (1918) online learning (1770) variance reduction (520) multi-armed bandit (1091) convex optimization (1320) convergence rate (606) non-convex optimization (546) stochastic gradient descent (1088) gradient descent (1143) nonconvex optimization (316)

Papers

High Probability Bounds for Stochastic Continuous Submodular Maximization AISTATS 2023
ASkewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks AISTATS 2023
Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits ICML 2023
Indirect Adversarial Losses via an Intermediate Distribution for Training GANs WACV 2023
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms NIPS 2023
Non-Convex Bilevel Optimization with Time-Varying Objective Functions NIPS 2023
Convergence of Adam Under Relaxed Assumptions NIPS 2023
Debiasing Conditional Stochastic Optimization NIPS 2023
Utilitarian Algorithm Configuration NIPS 2023
Adaptation to the Range in K-Armed Bandits JMLR 2023
Contextual Stochastic Bilevel Optimization NIPS 2023
Single-Call Stochastic Extragradient Methods for Structured Non-monotone Variational Inequalities: Improved Analysis under Weaker Conditions NIPS 2023
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization ICML 2023
Streaming Submodular Maximization with Differential Privacy ICML 2023
On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size JMLR 2023
Provable convergence guarantees for black-box variational inference NIPS 2023
Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure NIPS 2023
Learning Unnormalized Statistical Models via Compositional Optimization ICML 2023
Nash Regret Guarantees for Linear Bandits NIPS 2023
Buying Information for Stochastic Optimization ICML 2023
End-to-End Learning for Stochastic Optimization: A Bayesian Perspective ICML 2023
High-Probability Bounds for Stochastic Optimization and Variational Inequalities: the Case of Unbounded Variance ICML 2023
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM NIPS 2023
Decentralized Learning: Theoretical Optimality and Practical Improvements JMLR 2023
Delayed Algorithms for Distributed Stochastic Weakly Convex Optimization NIPS 2023