most citedPractical Comparison of Optimization Algorithms for Learning-Based MPC with Linear Models

8 citations · 8 across the 1 of their papers we have counts for

collaborators

5 papers

math.OC2024

Tensor Completion via Integer Optimization

Xin Chen, Sukanya Kudva, Yongzheng Dai +2

The main challenge with the tensor completion problem is a fundamental tension between computation power and the information-theoretic sample complexity rate. Past approaches eithe…

math.OC20241 cited

Optimal Contract Design for End-of-Life Care Payments

Muyan Jiang, Ying Chen, Xin Chen +2

A large fraction of total healthcare expenditure occurs due to end-of-life (EOL) care, which means it is important to study the problem of more carefully incentivizing necessary ve…

cs.LG2023

Estimating and Incentivizing Imperfect-Knowledge Agents with Hidden Rewards

Ilgin Dogan, Zuo-Jun Max Shen, Anil Aswani

In practice, incentive providers (i.e., principals) often cannot observe the reward realizations of incentivized agents, which is in contrast to many principal-agent models that ha…

cs.LG2023

Repeated Principal-Agent Games with Unobserved Agent Rewards and Perfect-Knowledge Agents

Ilgin Dogan, Zuo-Jun Max Shen, Anil Aswani

Motivated by a number of real-world applications from domains like healthcare and sustainable transportation, in this paper we study a scenario of repeated principal-agent games wi…

math.OC20148 cited

Practical Comparison of Optimization Algorithms for Learning-Based MPC with Linear Models

Anil Aswani, Patrick Bouffard, Xiaojing Zhang +1

Learning-based control methods are an attractive approach for addressing performance and efficiency challenges in robotics and automation systems. One such technique that has found…