13 citations · 28 across the 14 of their papers we have counts for
14 papers
Wasserstein Geodesic Generator for Conditional Distributions
Young-geun Kim, Kyungbok Lee, Youngwon Choi +2
Generating samples given a specific label requires estimating conditional distributions. We derive a tractable upper bound of the Wasserstein distance between conditional distribut…
Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert
Yoonhyung Lee, Sungdong Lee, Joong-Ho Won
The implicit stochastic gradient descent (ISGD), a proximal version of SGD, is gaining interest in the literature due to its stability over (explicit) SGD. In this paper, we conduc…
A unified analysis of convex and non-convex lp-ball projection problems
Joong-Ho Won, Kenneth Lange, Jason Xu
The task of projecting onto norm balls is ubiquitous in statistics and machine learning, yet the availability of actionable algorithms for doing so is largely limited to t…
Orthogonal Trace-Sum Maximization: Tightness of the Semidefinite Relaxation and Guarantee of Locally Optimal Solutions
Joong-Ho Won, Teng Zhang, Hua Zhou
This paper studies an optimization problem on the sum of traces of matrix quadratic forms in semi-orthogonal matrices, which can be considered as a generalization of the synchr…
Nonconvex Optimization via MM Algorithms: Convergence Theory
Kenneth Lange, Joong-Ho Won, Alfonso Landeros +1
The majorization-minimization (MM) principle is an extremely general framework for deriving optimization algorithms. It includes the expectation-maximization (EM) algorithm, proxim…
DistStat.jl: Towards Unified Programming for High-Performance Statistical Computing Environments in Julia
Seyoon Ko, Hua Zhou, Jin Zhou +1
The demand for high-performance computing (HPC) is ever-increasing for everyday statistical computing purposes. The downside is that we need to write specialized code for each HPC…