1 citations · 1 across the 9 of their papers we have counts for
10 papers
Hierarchical Utility Calibration for Structured Multiclass Decisions
Futoshi Futami, Jerry Huang, Ichiro Takeuchi
In multiclass probabilistic prediction, Utility Calibration (UC), which focuses auditing on specified utilities, has recently received attention as a way to guarantee downstream de…
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…
Multi-Scale Representation of Follicular Lymphoma Pathology Images in a Single Hyperbolic Space
Kei Taguchi, Kazumasa Ohara, Tatsuya Yokota +4
We propose a method for representing malignant lymphoma pathology images, from high-resolution cell nuclei to low-resolution tissue images, within a single hyperbolic space using s…
Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusion
Daiki Nishiyama, Hiroaki Miyoshi, Noriaki Hashimoto +4
Malignant lymphoma subtype classification directly impacts treatment strategies and patient outcomes, necessitating classification models that achieve both high accuracy and suffic…
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 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…
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…
Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping
Joshua Butke, Noriaki Hashimoto, Ichiro Takeuchi +3
Whole-slide image analysis via the means of computational pathology often relies on processing tessellated gigapixel images with only slide-level labels available. Applying multipl…