Research map / Learning theory and algorithms / Prediction and decision losses
Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models
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 0.96)
- Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization (ICLR 2025 · similarity 0.89)
- Wasserstein Distributionally Robust Optimization through the Lens of Structural Causal Models and Individual Fairness (NeurIPS 2024 · similarity 0.88)
- Knowledge-Guided Wasserstein Distributionally Robust Optimization (ICML 2025 · similarity 0.88)
- Achievable distributional robustness when the robust risk is only partially identified (NeurIPS 2024 · similarity 0.87)
- Estimating Generalization Performance Along the Trajectory of Proximal SGD in Robust Regression (NeurIPS 2024 · similarity 0.86)
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.
