479 citations · 1.1k across the 8 of their papers we have counts for
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cs.LG2023
Notes on Applicability of Explainable AI Methods to Machine Learning Models Using Features Extracted by Persistent Homology
Naofumi Hama
Data analysis that uses the output of topological data analysis as input for machine learning algorithms has been the subject of extensive research. This approach offers a means of…
cs.LG2022★ 1 cited
Model free variable importance for high dimensional data
Naofumi Hama, Masayoshi Mase, Art B. Owen
A model-agnostic variable importance method can be used with arbitrary prediction functions. Here we present some model-free methods that do not require access to the prediction fu…
cs.LG2022★ 4 cited
Deletion and Insertion Tests in Regression Models
Naofumi Hama, Masayoshi Mase, Art B. Owen
A basic task in explainable AI (XAI) is to identify the most important features behind a prediction made by a black box function . The insertion and deletion tests of Petsiuk et…