24 citations · 96 across the 31 of their papers we have counts for
7 papers · 1 filter
PerSPL: Personalized Semi-supervised Preference Learning
Xiaoan Xu, Tingzhou Wei, Juncheng Dong +3
Personalized reward models require user-specific preference comparisons, but collecting such feedback is costly and provides only limited supervision for each user. At the same tim…
Diffusion-Based Data-Driven Assortment Optimization
Junyi Liao, Xiaohui Jiang, Zhengwei Tong +2
Assortment optimization is a fundamental problem in revenue management, typically addressed using parametric choice models such as the multinomial logit (MNL) and its variants. Whi…
Decoding Rewards in Competitive Games: Inverse Game Theory with Entropy Regularization
Junyi Liao, Zihan Zhu, Ethan Fang +2
Estimating the unknown reward functions driving agents' behaviors is of central interest in inverse reinforcement learning and game theory. To tackle this problem, we develop a uni…
PASTA: Pessimistic Assortment Optimization
Juncheng Dong, Weibin Mo, Zhengling Qi +3
We consider a class of assortment optimization problems in an offline data-driven setting. A firm does not know the underlying customer choice model but has access to an offline da…
Implicit Regularization of Bregman Proximal Point Algorithm and Mirror Descent on Separable Data
Yan Li, Caleb Ju, Ethan X. Fang +1
Bregman proximal point algorithm (BPPA) has witnessed emerging machine learning applications, yet its theoretical understanding has been largely unexplored. We study the computatio…
Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy
Han Zhong, Xun Deng, Ethan X. Fang +3
While deep reinforcement learning has achieved tremendous successes in various applications, most existing works only focus on maximizing the expected value of total return and thu…
Inductive Bias of Gradient Descent based Adversarial Training on Separable Data
Yan Li, Ethan X. Fang, Huan Xu +1
Adversarial training is a principled approach for training robust neural networks. Despite of tremendous successes in practice, its theoretical properties still remain largely unex…