8 papers
Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-off
Maaya Sakata, Kazuto Fukuchi
Post-hoc controllability of fair machine learning models, the ability to control the trade-off between fairness and accuracy after training, is valuable for practical deployment. E…
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…
Robust Deep Reinforcement Learning against Adversarial Behavior Manipulation
Shojiro Yamabe, Kazuto Fukuchi, Jun Sakuma
This study investigates behavior-targeted attacks on reinforcement learning and their countermeasures. Behavior-targeted attacks aim to manipulate the victim's behavior as desired…
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…
Convergence rate of the (1+1)-evolution strategy on locally strongly convex functions with lipschitz continuous gradient
Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma +1
Evolution strategy (ES) is one of the promising classes of algorithms for black-box continuous optimization. Despite its broad successes in applications, theoretical analysis on th…
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…