Research map / Optimization / Nonconvex and smooth optimization
Generalization Bounds for (Wasserstein) Robust Optimization
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
- Universal generalization guarantees for Wasserstein distributionally robust models (ICLR 2025 · similarity 0.92)
- Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models (NeurIPS 2023 · similarity 0.91)
- Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization (NeurIPS 2022 · similarity 0.90)
- Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization (ICLR 2025 · similarity 0.88)
- Delving into the Convergence of Generalized Smooth Minimax Optimization (ICML 2024 · similarity 0.88)
- Algorithmic Stability Unleashed: Generalization Bounds with Unbounded Losses (ICML 2024 · 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.
