7 citations · 17 across the 6 of their papers we have counts for
7 papers
Langevin Autoencoders for Learning Deep Latent Variable Models
Shohei Taniguchi, Yusuke Iwasawa, Wataru Kumagai +1
Markov chain Monte Carlo (MCMC), such as Langevin dynamics, is valid for approximating intractable distributions. However, its usage is limited in the context of deep latent variab…
Equivariant and Invariant Reynolds Networks
Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai
Invariant and equivariant networks are useful in learning data with symmetry, including images, sets, point clouds, and graphs. In this paper, we consider invariant and equivariant…
Group Equivariant Conditional Neural Processes
Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai +2
We present the group equivariant conditional neural process (EquivCNP), a meta-learning method with permutation invariance in a data set as in conventional conditional neural proce…
Universal Approximation Theorem for Equivariant Maps by Group CNNs
Wataru Kumagai, Akiyoshi Sannai
Group symmetry is inherent in a wide variety of data distributions. Data processing that preserves symmetry is described as an equivariant map and often effective in achieving high…
Variable Selection for Nonparametric Learning with Power Series Kernels
Kota Matsui, Wataru Kumagai, Kenta Kanamori +2
In this paper, we propose a variable selection method for general nonparametric kernel-based estimation. The proposed method consists of two-stage estimation: (1) construct a consi…
Regret Analysis for Continuous Dueling Bandit
Wataru Kumagai
The dueling bandit is a learning framework wherein the feedback information in the learning process is restricted to a noisy comparison between a pair of actions. In this research,…