15 citations · 52 across the 14 of their papers we have counts for
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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…