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When Expressivity Meets Trainability: Fewer than Neurons Can Work
Jiawei Zhang, Yushun Zhang, Mingyi Hong +2
Modern neural networks are often quite wide, causing large memory and computation costs. It is thus of great interest to train a narrower network. However, training narrow neural n…
Structural Estimation of Markov Decision Processes in High-Dimensional State Space with Finite-Time Guarantees
Siliang Zeng, Mingyi Hong, Alfredo Garcia
We consider the task of estimating a structural model of dynamic decisions by a human agent based upon the observable history of implemented actions and visited states. This proble…
Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning
Sagar Shrestha, Xiao Fu, Mingyi Hong
This work revisits the joint beamforming (BF) and antenna selection (AS) problem, as well as its robust beamforming (RBF) version under imperfect channel state information (CSI). S…
Understanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective
Xinwei Zhang, Mingyi Hong, Nicola Elia
Distributed algorithms have been playing an increasingly important role in many applications such as machine learning, signal processing, and control. Significant research efforts…