2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
Offline Policy Learning with Weight Clipping and Heaviside Composite Optimization
Jingren Liu, Hanzhang Qin, Junyi Liu +2
Offline policy learning aims to use historical data to learn an optimal personalized decision rule. In the standard estimate-then-optimize framework, reweighting-based methods (e.g…
Statistical Robustness of Interval CVaR Based Regression Models under Perturbation and Contamination
Yulei You, Junyi Liu
Robustness under perturbation and contamination is a prominent issue in statistical learning. We address the robust nonlinear regression based on the so-called interval conditional…
Learning-Based Surrogate Method for Stochastic Optimization under Decision-Dependent Uncertainty with Adaptive Random Designs
Boyang Shen, Junyi Liu
We study stochastic programs in which the latent decision-dependent uncertainty is described via a nonparametric regression model. The major challenge is that, without convexity as…
Classification and Treatment Learning with Constraints via Composite Heaviside Optimization: a Progressive MIP Method
Yue Fang, Junyi Liu, Jong-Shi Pang
This paper proposes a Heaviside composite optimization approach and presents a progressive (mixed) integer programming (PIP) method for solving multi-class classification and multi…
Solving Nonsmooth Nonconvex Compound Stochastic Programs with Applications to Risk Measure Minimization
Junyi Liu, Ying Cui, Jong-Shi Pang
This paper studies a structured compound stochastic program (SP) involving multiple expectations coupled by nonconvex and nonsmooth functions. We present a successive convex-progra…