5 papers
Optimal Conformal Prediction under Epistemic Uncertainty
Alireza Javanmardi, Soroush H. Zargarbashi, Santo M. A. R. Thies +3
Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by constructing prediction sets with user-specified marginal coverage guarantees. In practi…
OperatorSHAP: Fast and Accurate Shapley Value Estimation for Neural Operators
Joshua Stiller, Santo M. A. R. Thies, Felix Czaja +1
Understanding model predictions is essential for physical applications, where outputs often inform safety-critical decisions, such as structural load assessment, weather warnings,…
Calibrated Preference Learning: The Case of Label Ranking
Santo M. A. R. Thies, Viktor Bengs, Timo Kaufmann +2
Calibration, the alignment of predicted probabilities with true outcome frequencies, is essential for reliable decision-making. While extensively studied for classification and reg…
Proxy-Based Approximation of Shapley and Banzhaf Interactions
Santo M. A. R. Thies, Hubert Baniecki, R. Teal Witter +3
Shapley and Banzhaf interactions capture the complex dynamics inherent in modern machine learning applications. However, current estimators for these higher-order interactions trad…
ConfoundingSHAP: Quantifying confounding strength in causal inference
Marie Brockschmidt, Santo M. A. R. Thies, Maresa Schröder +5
In causal inference, confounders are variables that influence both treatment decisions and outcomes. However, unlike as in randomized clinical trials, the treatment assignment mech…