3 papers
math.OC2025
Optimization of Bregman Variational Learning Dynamics
Jinho Cha, Youngchul Kim, Jungmin Shin +3
We develop a general optimization-theoretic framework for Bregman-Variational Learning Dynamics (BVLD), a new class of operator-based updates that unify Bayesian inference, mirror…
stat.CO2024
The R package psvmSDR: A Unified Algorithm for Sufficient Dimension Reduction via Principal Machines
Jungmin Shin, Seung Jun Shin, Andreas Artemiou
Sufficient dimension reduction (SDR), which seeks a lower-dimensional subspace of the predictors containing regression or classification information has been popular in a machine l…
stat.ME2024
A least distance estimator for a multivariate regression model using deep neural networks
Jungmin Shin, Seung Jun Shin, Sungwan Bang
We propose a deep neural network (DNN) based least distance (LD) estimator (DNN-LD) for a multivariate regression problem, addressing the limitations of the conventional methods. D…