9 citations · 50 across the 30 of their papers we have counts for
9 papers · 1 filter
Provable Data Scaling Law for Meta Learning via Complexity Minimization
Kazuto Fukuchi, Ryuichiro Hataya, Kota Matsui
Pre-training has become a fundamental paradigm in modern machine learning, with one of its key empirical benefits being reduced downstream sample complexity as the scale of pre-tra…
Provable Target Sample Complexity Improvements as Pre-Trained Models Scale
Kazuto Fukuchi, Ryuichiro Hataya, Kota Matsui
Pre-trained models have become indispensable for efficiently building models across a broad spectrum of downstream tasks. The advantages of pre-trained models have been highlighted…
Meta Optimality for Demographic Parity Constrained Regression via Post-Processing
Kazuto Fukuchi
We address the regression problem under the constraint of demographic parity, a commonly used fairness definition. Recent studies have revealed fair minimax optimal regression algo…
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift
Mitsuhiro Fujikawa, Yohei Akimoto, Jun Sakuma +1
Transfer learning enhances prediction accuracy on a target distribution by leveraging data from a source distribution, demonstrating significant benefits in various applications. T…
Faking Fairness via Stealthily Biased Sampling
Kazuto Fukuchi, Satoshi Hara, Takanori Maehara
Auditing fairness of decision-makers is now in high demand. To respond to this social demand, several fairness auditing tools have been developed. The focus of this study is to rai…
Differentially Private Empirical Risk Minimization with Input Perturbation
Kazuto Fukuchi, Quang Khai Tran, Jun Sakuma
We propose a novel framework for the differentially private ERM, input perturbation. Existing differentially private ERM implicitly assumed that the data contributors submit their…
Neutralized Empirical Risk Minimization with Generalization Neutrality Bound
Kazuto Fukuchi, Jun Sakuma
Currently, machine learning plays an important role in the lives and individual activities of numerous people. Accordingly, it has become necessary to design machine learning algor…
Differentially Private Analysis of Outliers
Rina Okada, Kazuto Fukuchi, Kazuya Kakizaki +1
This paper investigates differentially private analysis of distance-based outliers. The problem of outlier detection is to find a small number of instances that are apparently dist…
Fairness-Aware Learning with Restriction of Universal Dependency using f-Divergences
Kazuto Fukuchi, Jun Sakuma
Fairness-aware learning is a novel framework for classification tasks. Like regular empirical risk minimization (ERM), it aims to learn a classifier with a low error rate, and at t…