An effective self-interference cancellation scheme for spatial modulated full duplex systems. - Peizhong Ju; Miaowen Wen; Xiang Cheng 001; Liuqing Yang 001 (ICC, 2015)

Generalized spatial modulation with transmit antenna grouping for correlated channels. - Peizhong Ju; Meng Zhang 009; Xiang Cheng 001; Cheng-Xiang Wang 001; Liuqing Yang 001 (ICC, 2016)

Generalized spatial modulation with transmit antenna grouping for massive MIMO. - Peizhong Ju; Meng Zhang 009; Xiang Cheng 001; Liuqing Yang 001 (ICC, 2017)

Generalized Spatial Modulation With Transmit Antenna Grouping for Massive MIMO. - Weilin Qu; Meng Zhang 009; Xiang Cheng 001; Peizhong Ju (IEEE Access, 2017)

Adversarial Attacks to Distributed Voltage Control in Power Distribution Networks with DERs. - Peizhong Ju; Xiaojun Lin 001 (e-Energy, 2018)

Achievable-Rate-Enhancing Self-Interference Cancellation for Full-Duplex Communications. - Peizhong Ju; Miaowen Wen; Xiang Cheng 001; Liuqing Yang 001 (IEEE Trans. Wirel. Commun., 2018)

Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree. - Peizhong Ju; Xiaojun Lin 001; Jia Liu 002 (NeurIPS, 2020)

Overfitting Can Be Harmless for Basis Pursuit: Only to a Degree. - Peizhong Ju; Xiaojun Lin 001; Jia Liu 002 (CoRR, 2020)

On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models. - Peizhong Ju; Xiaojun Lin 001; Ness B. Shroff (ICML, 2021)

Understanding the Generalization Power of Overfitted NTK Models: 3-layer vs. 2-layer (Extended Abstract). - Peizhong Ju; Xiaojun Lin 001; Ness B. Shroff (Allerton, 2022)

Distribution-level markets under high renewable energy penetration. - Peizhong Ju; Xiaojun Lin 001; Jianwei Huang 001 (e-Energy, 2022)

On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model. - Peizhong Ju; Xiaojun Lin 001; Ness B. Shroff (NeurIPS, 2022)

Theoretical Characterization of the Generalization Performance of Overfitted Meta-Learning. - Peizhong Ju; Yingbin Liang; Ness B. Shroff (ICLR, 2023)

Theory on Forgetting and Generalization of Continual Learning. - Sen Lin 001; Peizhong Ju; Yingbin Liang; Ness B. Shroff (ICML, 2023)

Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning. - Peizhong Ju; Arnob Ghosh; Ness B. Shroff (CoRR, 2023)

Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes. - Peizhong Ju; Sen Lin 001; Mark S. Squillante; Yingbin Liang; Ness B. Shroff (CoRR, 2023)

Achieving Fairness in Multi-Agent MDP Using Reinforcement Learning. - Peizhong Ju; Arnob Ghosh; Ness B. Shroff (ICLR, 2024)

Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping. - Yining Li; Peizhong Ju; Ness B. Shroff (ICLR, 2024)

Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning? - Peizhong Ju; Haibo Yang 001; Jia Liu 002; Yingbin Liang; Ness B. Shroff (MobiHoc, 2024)

Efficient Multi-dimensional Compression for Network-edge Classification. - Chengzhang Li; Peizhong Ju; Atilla Eryilmaz; Ness B. Shroff (MobiHoc, 2024)

AI-EDGE: An NSF AI institute for future edge networks and distributed intelligence. - Peizhong Ju; Chengzhang Li; Yingbin Liang; Ness B. Shroff (AI Mag., 2024)

Non-asymptotic Convergence of Discrete-time Diffusion Models: New Approach and Improved Rate. - Yuchen Liang; Peizhong Ju; Yingbin Liang; Ness B. Shroff (CoRR, 2024)

PSMGD: Periodic Stochastic Multi-Gradient Descent for Fast Multi-Objective Optimization. - Mingjing Xu; Peizhong Ju; Jia Liu; Haibo Yang (AAAI, 2025)

How to Find the Exact Pareto Front for Multi-Objective MDPs? - Yining Li; Peizhong Ju; Ness B. Shroff (ICLR, 2025)

Broadening Target Distributions for Accelerated Diffusion Models via a Novel Analysis Approach. - Yuchen Liang; Peizhong Ju; Yingbin Liang; Ness B. Shroff (ICLR, 2025)

Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers. - Yuchen Liang; Peizhong Ju; Yingbin Liang; Ness B. Shroff (ICLR, 2025)

BeST - A Novel Source Selection Metric for Transfer Learning. - Ashutosh Soni; Peizhong Ju; Atilla Eryilmaz; Ness B. Shroff (CoRR, 2025)

Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution. - Muhammad Umair Haider; Hammad Rizwan; Hassan Sajjad 001; Peizhong Ju; A. B. Siddique 001 (CoRR, 2025)

FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation. - Srijith Nair; Michael Lin; Amirreza Talebi; Peizhong Ju; Elizabeth Bentley; Jia Liu (CoRR, 2025)

Unlocking the Power of Rehearsal in Continual Learning: A Theoretical Perspective. - Junze Deng; Qinhang Wu; Peizhong Ju; Sen Lin 001; Yingbin Liang; Ness B. Shroff (CoRR, 2025)

Evaluating Sparse Autoencoders for Monosemantic Representation. - Moghis Fereidouni; Muhammad Umair Haider; Peizhong Ju; A. B. Siddique 001 (CoRR, 2025)