2 citations · 2 across the 4 of their papers we have counts for
4 papers
How Does Preconditioning Guide Feature Learning in Deep Neural Networks?
Kotaro Yoshida, Atsushi Nitanda
Preconditioning is widely used in machine learning to accelerate convergence on the empirical risk, yet its role on the expected risk remains underexplored. In this work, we invest…
Mirror Descent Policy Optimisation for Robust Constrained Markov Decision Processes
David M. Bossens, Atsushi Nitanda
Safety is an essential requirement for reinforcement learning systems. The newly emerging framework of robust constrained Markov decision processes allows learning policies that sa…
Uniform convergence of the smooth calibration error and its relationship with functional gradient
Futoshi Futami, Atsushi Nitanda
Calibration is a critical requirement for reliable probabilistic prediction, especially in high-risk applications. However, the theoretical understanding of which learning algorith…
BODAME: Bilevel Optimization for Defense Against Model Extraction
Yuto Mori, Atsushi Nitanda, Akiko Takeda
Model extraction attacks have become serious issues for service providers using machine learning. We consider an adversarial setting to prevent model extraction under the assumptio…