most citedFairness-Aware Learning with Restriction of Universal Dependency using f-Divergences

1 citations · 1 across the 1 of their papers we have counts for

collaborators

3 papers

stat.ML2016

Efficiently Bounding Optimal Solutions after Small Data Modification in Large-Scale Empirical Risk Minimization

Hiroyuki Hanada, Atsushi Shibagaki, Jun Sakuma +1

We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorpo…

stat.ML2016

Secure Approximation Guarantee for Cryptographically Private Empirical Risk Minimization

Toshiyuki Takada, Hiroyuki Hanada, Yoshiji Yamada +2

Privacy concern has been increasingly important in many machine learning (ML) problems. We study empirical risk minimization (ERM) problems under secure multi-party computation (MP…

stat.ML2015★ 1 cited

Fairness-Aware Learning with Restriction of Universal Dependency using f-Divergences

Kazuto Fukuchi, Jun Sakuma

Fairness-aware learning is a novel framework for classification tasks. Like regular empirical risk minimization (ERM), it aims to learn a classifier with a low error rate, and at t…