7 papers
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…
Fast and More Powerful Selective Inference for Sparse High-order Interaction Model
Diptesh Das, Vo Nguyen Le Duy, Hiroyuki Hanada +2
Automated high-stake decision-making such as medical diagnosis requires models with high interpretability and reliability. As one of the interpretable and reliable models with good…
Supervised sequential pattern mining of event sequences in sport to identify important patterns of play: an application to rugby union
Rory Bunker, Keisuke Fujii, Hiroyuki Hanada +1
Given a set of sequences comprised of time-ordered events, sequential pattern mining is useful to identify frequent subsequences from different sequences or within the same sequenc…
Interval-based Prediction Uncertainty Bound Computation in Learning with Missing Values
Hiroyuki Hanada, Toshiyuki Takada, Jun Sakuma +1
The problem of machine learning with missing values is common in many areas. A simple approach is to first construct a dataset without missing values simply by discarding instances…
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…