75 citations · 459 across the 65 of their papers we have counts for
6 papers · 1 filter
Post Selection Inference with Kernels
Makoto Yamada, Yuta Umezu, Kenji Fukumizu +1
We propose a novel kernel based post selection inference (PSI) algorithm, which can not only handle non-linearity in data but also structured output such as multi-dimensional and m…
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…
Selective Inference Approach for Statistically Sound Predictive Pattern Mining
Shinya Suzumura, Kazuya Nakagawa, Mahito Sugiyama +2
Discovering statistically significant patterns from databases is an important challenging problem. The main obstacle of this problem is in the difficulty of taking into account the…
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…
Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining
Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama +2
In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database. Our main contr…
Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling
Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano +1
The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduce…