135 citations · 298 across the 23 of their papers we have counts for
5 papers · 1 filter
Nonconvex sparse regularization for deep neural networks and its optimality
Ilsang Ohn, Yongdai Kim
Recent theoretical studies proved that deep neural network (DNN) estimators obtained by minimizing empirical risk with a certain sparsity constraint can attain optimal convergence…
The semi-parametric Bernstein-von Mises theorem for regression models with symmetric errors
Minwoo Chae, Yongdai Kim, Bas Kleijn
In a smooth semi-parametric model, the marginal posterior distribution for a finite dimensional parameter of interest is expected to be asymptotically equivalent to the sampling di…
Calibrating nonconvex penalized regression in ultra-high dimension
Lan Wang, Yongdai Kim, Runze Li
We investigate high-dimensional nonconvex penalized regression, where the number of covariates may grow at an exponential rate. Although recent asymptotic theory established that t…
The Bernstein--von Mises theorem for the proportional hazard model
Yongdai Kim
We study large sample properties of Bayesian analysis of the proportional hazard model with neutral to the right process priors on the baseline hazard function. We show that the po…
A Bernstein-von Mises theorem in the nonparametric right-censoring model
Yongdai Kim, Jaeyong Lee
In the recent Bayesian nonparametric literature, many examples have been reported in which Bayesian estimators and posterior distributions do not achieve the optimal convergence ra…