11 papers · 1 filter
Safe Distributionally Robust Feature Selection under Covariate Shift
Hiroyuki Hanada, Satoshi Akahane, Noriaki Hashimoto +2
In practical machine learning, the environments encountered during the model development and deployment phases often differ, especially when a model is used by many users in divers…
Generalized Kernel Inducing Points by Duality Gap for Dataset Distillation
Tatsuya Aoyama, Hanting Yang, Hiroyuki Hanada +9
We propose Duality Gap KIP (DGKIP), an extension of the Kernel Inducing Points (KIP) method for dataset distillation. While existing dataset distillation methods often rely on bi-l…
Distributionally Robust Coreset Selection under Covariate Shift
Tomonari Tanaka, Hiroyuki Hanada, Hanting Yang +9
Coreset selection, which involves selecting a small subset from an existing training dataset, is an approach to reducing training data, and various approaches have been proposed fo…
Distributionally Robust Safe Sample Elimination under Covariate Shift
Hiroyuki Hanada, Tatsuya Aoyama, Satoshi Akahane +9
We consider a machine learning setup where one training dataset is used to train multiple models across slightly different data distributions. This occurs when customized models ar…
Distributionally Robust Safe Screening
Hiroyuki Hanada, Satoshi Akahane, Tatsuya Aoyama +8
In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method…
Efficient Model Selection for Predictive Pattern Mining Model by Safe Pattern Pruning
Takumi Yoshida, Hiroyuki Hanada, Kazuya Nakagawa +3
Predictive pattern mining is an approach used to construct prediction models when the input is represented by structured data, such as sets, graphs, and sequences. The main idea be…