2 citations · 2 across the 19 of their papers we have counts for
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Stable by Construction: Variational Latent Markov Operators for Long-Horizon PDE Prediction
Junyi Liao, Johann Guilleminot, Vahid Tarokh
Neural PDE solvers provide efficient surrogates for time-dependent physical systems, but autoregressive prediction over long horizons remains challenging because local errors can i…
Task-Based CT Protocol Optimization Using Reinforcement Learning and Virtual Imaging Trials
Jiaqi Zou, David Fenwick, Vahid Tarokh +6
Protocol optimization in computed tomography (CT) aims to improve diagnostic image quality while reducing radiation dose, but the interdependence of acquisition and reconstruction…
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…
Rethinking Token Prediction: Tree-Structured Diffusion Language Model
Zihao Wu, Haoming Yang, Juncheng Dong +1
Discrete diffusion language models have emerged as a competitive alternative to auto-regressive language models, but training them efficiently under limited parameter and memory bu…
Learning in Context, Guided by Choice: A Reward-Free Paradigm for Reinforcement Learning with Transformers
Juncheng Dong, Bowen He, Moyang Guo +3
In-context reinforcement learning (ICRL) leverages the in-context learning capabilities of transformer models (TMs) to efficiently generalize to unseen sequential decision-making t…
In-Context Reinforcement Learning From Suboptimal Historical Data
Juncheng Dong, Moyang Guo, Ethan X. Fang +2
Transformer models have achieved remarkable empirical successes, largely due to their in-context learning capabilities. Inspired by this, we explore training an autoregressive tran…
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…