Note - Robust Yet Efficient Conformal Prediction Sets

This repository contains a presentation I prepared for the paper “Robust Yet Efficient Conformal Prediction Sets” by Zargarbashi et al. (2024), presented at ICML 2024.

All core aspects—including the problem statement, methodology, intuition, results, and contributions—are clearly outlined in the accompanying slides. Please refer to the slides for the full details and visual breakdown.

Quick Overview

• Problem Addressed: Enhancing conformal prediction (CP) to withstand adversarial attacks such as evasion and poisoning.
• Approach: Deriving provably robust prediction sets by bounding worst-case changes in conformity scores.
• Outcome: More efficient and reliable prediction sets that maintain theoretical coverage guarantees across both continuous and discrete data modalities.

📄 Download slides overviewing Robust CP

Reference

Zargarbashi, Soroush H., Mohammad Sadegh Akhondzadeh, and Aleksandar Bojchevski. “Robust Yet Efficient Conformal Prediction Sets.” In International Conference on Machine Learning, 2024.