Research map / Adversarial robustness and security / Certified and empirical robustness
On Robustness of Linear Classifiers to Targeted Data Poisoning
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
- What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners? (NeurIPS 2023 · similarity 0.92)
- How Robust Are Randomized Smoothing Based Defenses to Data Poisoning? (CVPR 2021 · similarity 0.91)
- Not All Poisons are Created Equal: Robust Training against Data Poisoning (ICML 2022 · similarity 0.91)
- Like Oil and Water: Group Robustness Methods and Poisoning Defenses May Be at Odds (ICLR 2024 · similarity 0.88)
- Attack To Defend: Exploiting Adversarial Attacks for Detecting Poisoned Models (CVPR 2024 · similarity 0.87)
- Sharpness-Aware Data Poisoning Attack (ICLR 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.