3 citations · 3 across the 5 of their papers we have counts for
5 papers
Efficient Online Mirror Descent Stochastic Approximation for Multi-Stage Stochastic Programming
Junhui Zhang, Patrick Jaillet
We study the unconstrained and the minimax saddle point variants of the convex multi-stage stochastic programming problem, where consecutive decisions are coupled through the objec…
Multi-Timescale Primal Dual Hybrid Gradient with Application to Distributed Optimization
Junhui Zhang, Patrick Jaillet
We propose two variants of the Primal Dual Hybrid Gradient (PDHG) algorithm for saddle point problems with block decomposable duals, hereafter called Multi-Timescale PDHG (MT-PDHG)…
Secretary Problems with Random Number of Candidates: How Prior Distributional Information Helps
Junhui Zhang, Patrick Jaillet
We study variants of the secretary problem, where , the number of candidates, is a random variable, and the decision maker wants to maximize the probability of success -- pickin…
Memory-Constrained Algorithms for Convex Optimization via Recursive Cutting-Planes
Moïse Blanchard, Junhui Zhang, Patrick Jaillet
We propose a family of recursive cutting-plane algorithms to solve feasibility problems with constrained memory, which can also be used for first-order convex optimization. Precise…
Distributionally Constrained Black-Box Stochastic Gradient Estimation and Optimization
Henry Lam, Junhui Zhang
We consider stochastic gradient estimation using only black-box function evaluations, where the function argument lies within a probability simplex. This problem is motivated from…