1 citations · 1 across the 7 of their papers we have counts for
8 papers
Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners
Tinashe Handina, Tongxin Li, Kishan Panaganti +2
We study how an agent in a two-player repeated game can effectively utilize potentially imperfect advice when interacting with a no-regret learner. We characterize the advice lands…
Safety-Critical Contextual Control via Online Riemannian Optimization with World Models
Tongxin Li
Modern world models are becoming too complex to admit explicit dynamical descriptions. We study safety-critical contextual control, where a Planner must optimize a task objective u…
Quantum-Enabled Probabilistic Optimal Power Flow with Built-in Differential Privacy
Yuji Cao, Tongxin Li, Yue Chen
Quantum computing has been regarded as a promising approach to accelerate power system optimization. However, challenges such as limited qubits and inherent noise hinder their wide…
Reinforcement Learning with Imperfect Transition Predictions: A Bellman-Jensen Approach
Chenbei Lu, Zaiwei Chen, Tongxin Li +2
Traditional reinforcement learning (RL) assumes the agents make decisions based on Markov decision processes (MDPs) with one-step transition models. In many real-world applications…
Learning-Augmented Online Control for Decarbonizing Water Infrastructures
Jianyi Yang, Pengfei Li, Tongxin Li +2
Water infrastructures are essential for drinking water supply, irrigation, fire protection, and other critical applications. However, water pumping systems, which are key to transp…
Safe Exploitative Play with Untrusted Type Beliefs
Tongxin Li, Tinashe Handina, Shaolei Ren +1
The combination of the Bayesian game and learning has a rich history, with the idea of controlling a single agent in a system composed of multiple agents with unknown behaviors giv…
Building Socially-Equitable Public Models
Yejia Liu, Jianyi Yang, Pengfei Li +2
Public models offer predictions to a variety of downstream tasks and have played a crucial role in various AI applications, showcasing their proficiency in accurate predictions. Ho…
Anytime-Competitive Reinforcement Learning with Policy Prior
Jianyi Yang, Pengfei Li, Tongxin Li +2
This paper studies the problem of Anytime-Competitive Markov Decision Process (A-CMDP). Existing works on Constrained Markov Decision Processes (CMDPs) aim to optimize the expected…