activity
20112026
most citedLearning to Optimize: Training Deep Neural Networks for Wireless Resource Management

895 citations · 5.3k across the 104 of their papers we have counts for

collaborators

105 papers

cs.LG2026

Fully Distributed GNE Algorithms for Multi-Robot Placement without Consensus on Multipliers

Shao-An Yin, Mingyi Hong, Nicola Elia

Recent machine learning research has increasingly focused on equilibrium analysis in non-cooperative games rather than solely on optimal solutions. Many such problems involve share…

cs.LG2022

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…

cs.LG2022★ 1 cited

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…

eess.SP2022★ 28 cited

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…

cs.LG2022

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…

cs.LG2021★ 7 cited

Dynamic Differential-Privacy Preserving SGD

Jian Du, Song Li, Xiangyi Chen +2

The vanilla Differentially-Private Stochastic Gradient Descent (DP-SGD), including DP-Adam and other variants, ensures the privacy of training data by uniformly distributing privac…