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cs.LG2026
Noisy but Valid: Robust Statistical Evaluation of LLMs with Imperfect Judges
Chen Feng, Minghe Shen, Ananth Balashankar +2
Reliable certification of Large Language Models (LLMs)-verifying that failure rates are below a safety threshold-is critical yet challenging. While "LLM-as-a-Judge" offers scalabil…
cs.LG2024★ 1 cited
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Models
Daqin Luo, Chengjian Feng, Yuxuan Nong +1
Automated Machine Learning (AutoML) offers a promising approach to streamline the training of machine learning models. However, existing AutoML frameworks are often limited to unim…
cs.LG2024
PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
Chen Feng, Ziquan Liu, Zhuo Zhi +3
It is widely known that state-of-the-art machine learning models, including vision and language models, can be seriously compromised by adversarial perturbations. It is therefore i…