Author name cluster

Tsuyoshi Idé

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4 papers

2 author rows

AAAI Conference 2021 Conference Paper

Anomaly Attribution with Likelihood Compensation

Tsuyoshi Idé
Amit Dhurandhar
Jiří Navrátil
Moninder Singh
Naoki Abe

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 from many sensor measurements, we often have a situation where some observed samples may significantly deviate from their prediction. It may be due to a sub-optimal black-box model, or simply because those samples are outliers. In either case, one would ideally want to compute a “responsibility score” indicative of the extent to which an input variable is responsible for the anomalous output. In this work, we formalize this task as a statistical inverse problem: Given model deviation from the expected value, infer the responsibility score of each of the input variables. We propose a new method called likelihood compensation (LC), which is founded on the likelihood principle and computes a correction to each input variable. To the best of our knowledge, this is the first principled framework that computes a responsibility score for real valued anomalous model deviations. We apply our approach to a real-world building energy prediction task and confirm its utility based on expert feedback.

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IJCAI Conference 2019 Conference Paper

Efficient Protocol for Collaborative Dictionary Learning in Decentralized Networks

Tsuyoshi Idé
Rudy Raymond
Dzung T. Phan

This paper is concerned with the task of collaborative density estimation in the distributed multi-task setting. Major application scenarios include collaborative anomaly detection among distributed industrial assets owned by different companies competing with each other. Of critical importance here is to achieve two conflicting goals at once: data privacy and collaboration. To this end, we propose a new framework for collaborative dictionary learning. By using a mixture of the exponential family, we show that collaborative learning can be nicely separated into three steps: local updates, global consensus, and optimization. For the critical step of consensus building, we propose a new algorithm that does not rely on expensive encryption-based multi-party computation. Our theoretical and experimental analysis shows that our method is several orders of magnitude faster than the alternative.

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AAAI Conference 2019 Conference Paper

Tensorial Change Analysis Using Probabilistic Tensor Regression

Tsuyoshi Idé

This paper proposes a new method for change detection and analysis using tensor regression. Change detection in our setting is to detect changes in the relationship between the input tensor and the output scalar while change analysis is to compute the responsibility score of individual tensor modes and dimensions for the change detected. We develop a new probabilistic tensor regression method, which can be viewed as a probabilistic generalization of the alternating least squares algorithm. Thanks to the probabilistic formulation, the derived change scores have a clear information-theoretic interpretation. We apply our method to semiconductor manufacturing to demonstrate the utility. To the best of our knowledge, this is the first work of change analysis based on probabilistic tensor regression.

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ECAI Conference 2014 Conference Paper

Probabilistic Two-Level Anomaly Detection for Correlated Systems

Bin Tong
Tetsuro Morimura
Einoshin Suzuki
Tsuyoshi Idé

We propose a novel probabilistic semi-supervised anomaly detection framework for multi-dimensional systems with high correlation among variables. Our method is able to identify both abnormal instances and abnormal variables of an instance.

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