5 citations · 7 across the 4 of their papers we have counts for
4 papers
Mitigating LLM Hallucinations via Conformal Abstention
Yasin Abbasi Yadkori, Ilja Kuzborskij, David Stutz +9
We develop a principled procedure for determining when a large language model (LLM) should abstain from responding (e.g., by saying "I don't know") in a general domain, instead of…
Conformalized Credal Set Predictors
Alireza Javanmardi, David Stutz, Eyke Hüllermeier
Credal sets are sets of probability distributions that are considered as candidates for an imprecisely known ground-truth distribution. In machine learning, they have recently attr…
Certified Robust Models with Slack Control and Large Lipschitz Constants
Max Losch, David Stutz, Bernt Schiele +1
Despite recent success, state-of-the-art learning-based models remain highly vulnerable to input changes such as adversarial examples. In order to obtain certifiable robustness aga…
Unlocking Accuracy and Fairness in Differentially Private Image Classification
Leonard Berrada, Soham De, Judy Hanwen Shen +6
Privacy-preserving machine learning aims to train models on private data without leaking sensitive information. Differential privacy (DP) is considered the gold standard framework…