21 citations · 34 across the 31 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
Truthful Elicitation of Imprecise Forecasts
Anurag Singh, Siu Lun Chau, Krikamol Muandet
The quality of probabilistic forecasts is crucial for decision-making under uncertainty. While proper scoring rules incentivize truthful reporting of precise forecasts, they fall s…
Bayesian Optimization for Building Social-Influence-Free Consensus
Masaki Adachi, Siu Lun Chau, Wenjie Xu +3
We introduce Social Bayesian Optimization (SBO), a vote-efficient algorithm for consensus-building in collective decision-making. In contrast to single-agent scenarios, collective…
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…