Research map / Learning theory and algorithms / Sample complexity
Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization
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
- Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness (NeurIPS 2022 · similarity 0.91)
- Adversarially Robust Generalization Requires More Data (NeurIPS 2018 · similarity 0.91)
- Probabilistically Robust Learning: Balancing Average and Worst-case Performance (ICML 2022 · similarity 0.90)
- Theoretical Analysis of Adversarial Learning: A Minimax Approach (NeurIPS 2019 · similarity 0.90)
- A Spectral View of Adversarially Robust Features (NeurIPS 2018 · similarity 0.90)
- Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data (ICLR 2025 · similarity 0.89)
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.
