60 citations · 196 across the 20 of their papers we have counts for
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cs.LG2020★ 3 cited
Differentially Private Variational Autoencoders with Term-wise Gradient Aggregation
Tsubasa Takahashi, Shun Takagi, Hajime Ono +1
This paper studies how to learn variational autoencoders with a variety of divergences under differential privacy constraints. We often build a VAE with an appropriate prior distri…
cs.LG2020★ 19 cited
Locally Private Distributed Reinforcement Learning
Hajime Ono, Tsubasa Takahashi
We study locally differentially private algorithms for reinforcement learning to obtain a robust policy that performs well across distributed private environments. Our algorithm pr…
cs.LG2018★ 2 cited
Lightweight Lipschitz Margin Training for Certified Defense against Adversarial Examples
Hajime Ono, Tsubasa Takahashi, Kazuya Kakizaki
How can we make machine learning provably robust against adversarial examples in a scalable way? Since certified defense methods, which ensure -robust, consume huge resources, t…