Siu Lun Chau

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: $ε$ -Exact Shapley Values for Product Games in Logarithmic Parallel Time

Majid Mohammadi, Grigory Reznikov, Pavel Sinitcyn +2

We introduce QuadraSHAP, a method for $ε$ -exact Shapley computation in product games, cooperative games whose coalition values factorize across players. Given a tolerance $ε \geq 0$ ,…

stat.ML2026

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…

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…

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…

When Do Credal Sets Stabilize? Fixed-Point Theorems for Credal Set Updates

Michele Caprio, Siu Lun Chau, Krikamol Muandet

Many machine learning algorithms rely on iterative updates of uncertainty representations, ranging from variational inference and expectation-maximization, to reinforcement learnin…

Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes

Majid Mohammadi, Krikamol Muandet, Ilaria Tiddi +2

Shapley values are widely recognized as a principled method for attributing importance to input features in machine learning. However, the exact computation of Shapley values scale…

stat.ML2025

Kernel Quantile Embeddings and Associated Probability Metrics

Masha Naslidnyk, Siu Lun Chau, François-Xavier Briol +1

Embedding probability distributions into reproducing kernel Hilbert spaces (RKHS) has enabled powerful nonparametric methods such as the maximum mean discrepancy (MMD), a statistic…

Amortized Linear-time Exact Shapley Value for Product-Kernel Methods

Majid Mohammadi, Siu Lun Chau, Krikamol Muandet

Kernel methods are widely used in machine learning and statistics for their flexibility and expressive power, yet their black-box nature limits adoption in high-stakes applications…

stat.ML2025

Integral Imprecise Probability Metrics

Siu Lun Chau, Michele Caprio, Krikamol Muandet

Quantifying differences between probability distributions is fundamental to statistics and machine learning, primarily for comparing statistical uncertainty. In contrast, epistemic…