4 papers
Logic-Logit: A Logic-Based Approach to Choice Modeling
Shuhan Zhang, Wendi Ren, Shuang Li
In this study, we propose a novel rule-based interpretable choice model, Logic-Logit, designed to effectively learn and explain human choices. Choice models have been widely applie…
Variance-Optimal Control Variates for Learning with Black-box Feedback
Zihao Zhao, Shuhan Zhang, Kai Wang
Modern models increasingly learn through black-box oracles such as humans, optimization solvers, and external tools that provide feedback without exposing their internal mechanisms…
Wasserstein Proximal Policy Gradient
Zhaoyu Zhu, Shuhan Zhang, Rui Gao +1
We study policy gradient methods for continuous-action, entropy-regularized reinforcement learning through the lens of Wasserstein geometry. Starting from a Wasserstein proximal up…
DeepHalo: A Neural Choice Model with Controllable Context Effects
Shuhan Zhang, Zhi Wang, Rui Gao +1
Modeling human decision-making is central to applications such as recommendation, preference learning, and human-AI alignment. While many classic models assume context-independent…