5 citations · 18 across the 17 of their papers we have counts for
17 papers
Dropout Drops Double Descent
Tian-Le Yang, Joe Suzuki
This study demonstrates that double descent can be mitigated by adding a dropout layer adjacent to the fully connected linear layer. The unexpected double-descent phenomenon garner…
Newton-type Methods with the Proximal Gradient Step for Sparse Estimation
Ryosuke Shimmura, Joe Suzuki
In this paper, we propose new methods to efficiently solve convex optimization problems encountered in sparse estimation, which include a new quasi-Newton method that avoids comput…
Causal Order Identification to Address Confounding: Binary Variables
Joe Suzuki, Yusuke Inaoka
This paper considers an extension of the linear non-Gaussian acyclic model (LiNGAM) that determines the causal order among variables from a dataset when the variables are expressed…
Efficient proximal gradient algorithms for joint graphical lasso
Jie Chen, Ryosuke Shimmura, Joe Suzuki
We consider learning an undirected graphical model from sparse data. While several efficient algorithms have been proposed for graphical lasso (GL), the alternating direction metho…
Converting ADMM to a Proximal Gradient for Efficient Sparse Estimation
Ryosuke Shimmura, Joe Suzuki
In sparse estimation, such as fused lasso and convex clustering, we apply either the proximal gradient method or the alternating direction method of multipliers (ADMM) to solve the…
Forest Learning from Data and its Universal Coding
Joe Suzuki
This paper considers structure learning from data with samples of variables, assuming that the structure is a forest, using the Chow-Liu algorithm. Specifically, for incomp…