51 citations · 99 across the 7 of their papers we have counts for
7 papers
FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters
Hiro Ishii, Kenta Niwa, Hiroshi Sawada +3
We propose Federated Preconditioned Mixing (FedPM), a novel Federated Learning (FL) method that leverages second-order optimization. Prior methods--such as LocalNewton, LTDA, and F…
KIE: Kernel Method-based Kernel Intensity Estimators for Inhomogeneous Poisson Processes
Hideaki Kim, Tomoharu Iwata, Akinori Fujino
Kernel method-based intensity estimators, formulated within reproducing kernel Hilbert spaces (RKHSs), and classical kernel intensity estimators (KIEs) have been among the most eas…
Evacuation Shelter Scheduling Problem
Hitoshi Shimizu, Hirohiko Suwa, Tomoharu Iwata +3
Evacuation shelters, which are urgently required during natural disasters, are designed to minimize the burden of evacuation on human survivors. However, the larger the scale of th…
Learning Individually Fair Classifier with Path-Specific Causal-Effect Constraint
Yoichi Chikahara, Shinsaku Sakaue, Akinori Fujino +1
Machine learning is used to make decisions for individuals in various fields, which require us to achieve good prediction accuracy while ensuring fairness with respect to sensitive…
Partial AUC Maximization via Nonlinear Scoring Functions
Naonori Ueda, Akinori Fujino
We propose a method for maximizing a partial area under a receiver operating characteristic (ROC) curve (pAUC) for binary classification tasks. In binary classification tasks, accu…
Polynomial Networks and Factorization Machines: New Insights and Efficient Training Algorithms
Mathieu Blondel, Masakazu Ishihata, Akinori Fujino +1
Polynomial networks and factorization machines are two recently-proposed models that can efficiently use feature interactions in classification and regression tasks. In this paper,…
Higher-Order Factorization Machines
Mathieu Blondel, Akinori Fujino, Naonori Ueda +1
Factorization machines (FMs) are a supervised learning approach that can use second-order feature combinations even when the data is very high-dimensional. Unfortunately, despite i…