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From the 1 of 21 linked papers with an AI index.

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21 papers

cs.AI2026

Quantification of Credal Uncertainty: A Distance-Based Approach

Xabier Gonzalez-Garcia, Siu Lun Chau, Julian Rodemann +6

The paper introduces a distance-based method using Integral Probability Metrics to quantify total, aleatoric, and epistemic uncertainty for credal sets, providing efficient measure…

cs.LG2026

Incentive Aware AI Regulations: A Credal Characterisation

Anurag Singh, Julian Rodemann, Rajeev Verma +2

The rapid proliferation of AI applications has intensified debate on effective regulation of these black-box services. Effective regulation must balance two competing goals: (1) de…

stat.ML2026

Instrumental and Proximal Causal Inference with Gaussian Processes

Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic +2

Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding. Despite substantial m…

stat.ML2026

Performative Learning Theory

Julian Rodemann, Unai Fischer-Abaigar, James Bailie +1

Performative predictions influence the very outcomes they aim to forecast. We study performative predictions that affect a sample (e.g., only existing users of an app) and/or the w…

cs.AI2026

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.…

cs.LG2026

QuadraSHAP: Stable and Scalable Shapley Values for Product Games via Gauss-Legendre Quadrature

Majid Mohammadi, Grigory Reznikov, Pavel Sinitcyn +2

We study the efficient computation of Shapley values for \emph{product games} -- cooperative games in which the coalition value factorizes as a product of per-player terms. Such ga…