Publications (17)
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…
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty
Yu Inatsu, Shion Takeno, Hiroyuki Hanada +2
In this study, we propose a novel multi-objective Bayesian optimization (MOBO) method to efficiently identify the Pareto front (PF) defined by risk measures for black-box functions…
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…
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…
Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications
Hiroyuki Hanada, Noriaki Hashimoto, Kouichi Taji +1
In this study, we have developed an incremental machine learning (ML) method that efficiently obtains the optimal model when a small number of instances or features are added or re…
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…
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…
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…
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…
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…
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…
Distributionally Robust Active Learning for Gaussian Process Regression
Shion Takeno, Yoshito Okura, Yu Inatsu +9
Gaussian process regression (GPR) or kernel ridge regression is a widely used and powerful tool for nonlinear prediction. Therefore, active learning (AL) for GPR, which actively co…
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…
On Practical Accuracy of Edit Distance Approximation Algorithms
Hiroyuki Hanada, Mineichi Kudo, Atsuyoshi Nakamura
The edit distance is a basic string similarity measure used in many applications such as text mining, signal processing, bioinformatics, and so on. However, the computational cost…
Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening
Hiroki Kato, Hiroyuki Hanada, Ichiro Takeuchi
We consider the problem of learning a sparse rule model, a prediction model in the form of a sparse linear combination of rules, where a rule is an indicator function defined over…
Conditional Latent Space Molecular Scaffold Optimization for Accelerated Molecular Design
Onur Boyar, Hiroyuki Hanada, Ichiro Takeuchi
The rapid discovery of new chemical compounds is essential for advancing global health and developing treatments. While generative models show promise in creating novel molecules,…
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…