4 citations · 8 across the 26 of their papers we have counts for
4 papers · 1 filter
Off-Policy Evaluation with Strategic Agents via Local Disclosure
Kiet Q. H. Vo, Abbavaram Gowtham Reddy, Julian Rodemann +2
We study off-policy evaluation (OPE) under strategic behavior where decision subjects (or agents) respond to a decision maker's policy by strategically modifying their covariates.…
Quantification of Credal Uncertainty: A Distance-Based Approach
Xabier Gonzalez-Garcia, Siu Lun Chau, Julian Rodemann +6
Credal sets, i.e., closed convex sets of probability measures, provide a natural framework to represent aleatoric and epistemic uncertainty in machine learning. Yet how to quantify…
A Statistical Case Against Empirical Human-AI Alignment
Julian Rodemann, Esteban Garces Arias, Christoph Luther +2
Empirical human-AI alignment aims to make AI systems act in line with observed human behavior. While noble in its goals, we argue that empirical alignment can inadvertently introdu…
Accounting for Gaussian Process Imprecision in Bayesian Optimization
Julian Rodemann, Thomas Augustin
Bayesian optimization (BO) with Gaussian processes (GP) as surrogate models is widely used to optimize analytically unknown and expensive-to-evaluate functions. In this paper, we p…