3 citations · 4 across the 21 of their papers we have counts for
5 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…
Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities
Anita Yang, Krikamol Muandet, Michele Caprio +2
Despite the growing demand for eliciting uncertainty from large language models (LLMs), empirical evidence suggests that LLM behavior is not always adequately captured by the elici…
Explanation Design in Strategic Learning: Sufficient Explanations that Induce Non-harmful Responses
Kiet Q. H. Vo, Siu Lun Chau, Masahiro Kato +2
We study explanation design in algorithmic decision making with strategic agents, individuals who may modify their inputs in response to explanations of a decision maker's (DM's) p…
Causal Strategic Learning with Competitive Selection
Kiet Q. H. Vo, Muneeb Aadil, Siu Lun Chau +1
We study the problem of agent selection in causal strategic learning under multiple decision makers and address two key challenges that come with it. Firstly, while much of prior w…