22 citations · 24 across the 10 of their papers we have counts for
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math.OC2020★ 1 cited
Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension
Shotaro Yagishita, Jun-ya Gotoh
Network Lasso (NL for short) is a methodology for estimating models by simultaneously clustering data samples and fitting the models to the samples. It often succeeds in forming cl…
econ.EM2020
Worst-case sensitivity
Jun-ya Gotoh, Michael Jong Kim, Andrew E. B. Lim
We introduce the notion of Worst-Case Sensitivity, defined as the worst-case rate of increase in the expected cost of a Distributionally Robust Optimization (DRO) model when the si…
math.OC2020★ 1 cited
On the superiority of PGMs to PDCAs in nonsmooth nonconvex sparse regression
Shummin Nakayama, Jun-ya Gotoh
This paper conducts a comparative study of proximal gradient methods (PGMs) and proximal DC algorithms (PDCAs) for sparse regression problems which can be cast as Difference-of-two…