Research map / Optimization / Nonconvex and smooth optimization
Sifting through the noise: Universal first-order methods for stochastic variational inequalities
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Adaptive and Universal Algorithms for Variational Inequalities with Optimal Convergence (AAAI 2022 · similarity 0.92)
- First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities (NeurIPS 2023 · similarity 0.92)
- Generalized Smooth Stochastic Variational Inequalities: Almost Sure Convergence and Convergence Rates (NeurIPS 2026 · similarity 0.89)
- High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise (ICML 2024 · similarity 0.89)
- Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction (NeurIPS 2024 · similarity 0.88)
- Last-Iterate Convergence of Single-Loop Stochastic Methods for Constrained Convex-Concave Minimax Problems (NeurIPS 2026 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
