4 papers
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison
Kensuke Mitsuzawa, Motonobu Kanagawa, Stefano Bortoli +2
We study two-sample variable selection: identifying variables that discriminate between the distributions of two sets of data vectors. Such variables help scientists understand the…
MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations
Kensuke Mitsuzawa, Damien Garreau
Large Language Models (LLMs) are increasingly integrated into agentic AI systems, yet their propensity to generate hallucinations remains a critical safety concern. Detecting these…
Word Sense Detection Leveraging Maximum Mean Discrepancy
Kensuke Mitsuzawa
Word sense analysis is an essential analysis work for interpreting the linguistic and social backgrounds. The word sense change detection is a task of identifying and interpreting…
Variable Selection for Comparing High-dimensional Time-Series Data
Kensuke Mitsuzawa, Margherita Grossi, Stefano Bortoli +1
Given a pair of multivariate time-series data of the same length and dimensions, an approach is proposed to select variables and time intervals where the two series are significant…