6 citations · 11 across the 3 of their papers we have counts for
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cs.LG2024
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
Dongxia Wu, Tsuyoshi Idé, Aurélie Lozano +5
We address the problem of learning Granger causality from asynchronous, interdependent, multi-type event sequences. In particular, we are interested in discovering instance-level c…
cs.LG2023★ 5 cited
Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution
Tsuyoshi Idé, Naoki Abe
We address the task of probabilistic anomaly attribution in the black-box regression setting, where the goal is to compute the probability distribution of the attribution score of…
cs.LG2022★ 6 cited
Anomaly Attribution with Likelihood Compensation
Tsuyoshi Idé, Amit Dhurandhar, Jiří Navrátil +2
This paper addresses the task of explaining anomalous predictions of a black-box regression model. When using a black-box model, such as one to predict building energy consumption…