2 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
Learning Robust Treatment Rules for Censored Data
Yifan Cui, Junyi Liu, Tao Shen +2
There is a fast-growing literature on estimating optimal treatment rules directly by maximizing the expected outcome. In biomedical studies and operations applications, censored su…
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…