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Hiroki Arimura

3 accepted papers

2021

Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization

AAAI 2021technical

Post-hoc explanation methods for machine learning models have been widely used to support decision-making. One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result.…

2020

DACE: Distribution-Aware Counterfactual Explanation by Mixed-Integer Linear Optimization

IJCAI 2020poster

Counterfactual Explanation (CE) is one of the post-hoc explanation methods that provides a perturbation vector so as to alter the prediction result obtained from a classifier. Users can directly interpret the perturbation as an "action" for obtaining their desired decision results. However, an actio…

Cited by 0SourcePDFScholar
2017

On the Model Shrinkage Effect of Gamma Process Edge Partition Models

NeurIPS 2017poster

The edge partition model (EPM) is a fundamental Bayesian nonparametric model for extracting an overlapping structure from binary matrix. The EPM adopts a gamma process ($\Gamma$P) prior to automatically shrink the number of active atoms. However, we empirically found that the model shrinkage of the…

Cited by 6SourcePDFScholar