8 citations · 17 across the 7 of their papers we have counts for
7 papers
An Analysis of Selection Bias Issue for Online Advertising
Shinya Suzumura, Hitoshi Abe
In online advertising, a set of potential advertisements can be ranked by a certain auction system where usually the top-1 advertisement would be selected and displayed at an adver…
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…
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…
Homotopy Continuation Approaches for Robust SV Classification and Regression
Shinya Suzumura, Kohei Ogawa, Masashi Sugiyama +2
In support vector machine (SVM) applications with unreliable data that contains a portion of outliers, non-robustness of SVMs often causes considerable performance deterioration. A…
Safe Feature Pruning for Sparse High-Order Interaction Models
Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama +2
Taking into account high-order interactions among covariates is valuable in many practical regression problems. This is, however, computationally challenging task because the numbe…
An Efficient Post-Selection Inference on High-Order Interaction Models
S. Suzumura, K. Nakagawa, K. Tsuda +1
Finding statistically significant high-order interaction features in predictive modeling is important but challenging task. The difficulty lies in the fact that, for a recent appli…
Safe Sample Screening for Support Vector Machines
Kohei Ogawa, Yoshiki Suzuki, Shinya Suzumura +1
Sparse classifiers such as the support vector machines (SVM) are efficient in test-phases because the classifier is characterized only by a subset of the samples called support vec…