21 citations · 34 across the 31 of their papers we have counts for
13 papers · 1 filter
Open-ended Scientific Discovery with Possibilistic Reasoning
Anita Yang, Siu Lun Chau, Tomoya Wakayama +2
Autonomous scientific discovery with LLMs requires generating and testing hypotheses adaptively as evidence accumulates while maintaining statistical validity. Existing anytime-val…
Epistemic Learning from Imprecise Annotation
Kaizheng Wang, Siu Lun Chau
Imprecise annotations may support several plausible labelling distributions, yet learning methods often resolve this ambiguity into a single predictive distribution. This can obscu…
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.…
QuadraSHAP: -Exact Shapley Values for Product Games in Logarithmic Parallel Time
Majid Mohammadi, Grigory Reznikov, Pavel Sinitcyn +2
We introduce QuadraSHAP, a method for -exact Shapley computation in product games, cooperative games whose coalition values factorize across players. Given a tolerance ,…
Measuring Differences between Conditional Distributions using Kernel Embeddings
Peter Moskvichev, Siu Lun Chau, Dino Sejdinovic
Comparing conditional distributions is a fundamental challenge in statistics and machine learning, with applications across a wide range of domains. While proposed methods for meas…
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…
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…
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…
Beyond Prediction-Set Size: Quantifying Conformal Uncertainty through Credal Geometry
Siu Lun Chau, Soroush H. Zargarbashi, Yusuf Sale +1
Prediction-set size offers a coarse summary of conformal uncertainty. We develop a richer perspective through the geometry of credal sets: families of probability distributions com…
Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study
Kaizheng Wang, Yunjia Wang, Fabio Cuzzolin +3
Epistemic uncertainty in neural networks is commonly modeled using two second-order paradigms: distribution-based representations, which rely on posterior parameter distributions,…
Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features
Michele Caprio, Katerina Papagiannouli, Siu Lun Chau +1
We propose a robust Bayesian formulation of random feature (RF) regression that accounts explicitly for prior and likelihood misspecification via Huber-style contamination sets. St…
Learning Credal Ensembles via Distributionally Robust Optimization
Kaizheng Wang, Ghifari Adam Faza, Fabio Cuzzolin +3
Credal predictors are models that are aware of epistemic uncertainty and produce a convex set of probabilistic predictions. They offer a principled way to quantify predictive epist…