178 citations · 241 across the 10 of their papers we have counts for
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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…
Black-Box Anomaly Attribution
Tsuyoshi Idé, Naoki Abe
When the prediction of a black-box machine learning model deviates from the true observation, what can be said about the reason behind that deviation? This is a fundamental and ubi…
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…
Cardinality-Regularized Hawkes-Granger Model
Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan +1
We propose a new sparse Granger-causal learning framework for temporal event data. We focus on a specific class of point processes called the Hawkes process. We begin by pointing o…