| 2025 | ICML | Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs. | Ziang Chen, Xiaohan Chen, Jialin Liu, Xinshang Wang, Wotao Yin |
| 2023 | AAAI | Safeguarded Learned Convex Optimization. | Howard Heaton, Xiaohan Chen, Zhangyang Wang, Wotao Yin |
| 2023 | CVPR | Many-Task Federated Learning: A New Problem Setting and A Simple Baseline. | Ruisi Cai, Xiaohan Chen, Shiwei Liu, Jayanth Srinivasa, Myungjin Lee, Ramana Kompella, Zhangyang Wang |
| 2023 | ICLR | More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. | Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Tommi Krkkinen, Mykola Pechenizkiy, Decebal Constantin Mocanu, Zhangyang Wang |
| 2023 | ICML | Towards Constituting Mathematical Structures for Learning to Optimize. | Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin, HanQin Cai |
| 2022 | AAAI | Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better. | Sameer Bibikar, Haris Vikalo, Zhangyang Wang, Xiaohan Chen |
| 2022 | ICLR | Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently. | Xiaohan Chen, Jason Zhang, Zhangyang Wang |
| 2022 | ICLR | Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity. | Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu |
| 2022 | ICLR | The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training. | Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy |
| 2021 | ACL | EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets. | Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan, Zhangyang Wang, Jingjing Liu |
| 2021 | ICLR | A Design Space Study for LISTA and Beyond. | Tianjian Meng, Xiaohan Chen, Yifan Jiang, Zhangyang Wang |
| 2021 | ICLR | Learning A Minimax Optimizer: A Pilot Study. | Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, Zhangyang Wang |
| 2020 | AISTATS | Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery. | Zepeng Huo, Arash Pakbin, Xiaohan Chen, Nathan C. Hurley, Ye Yuan, Xiaoning Qian, Zhangyang Wang, Shuai Huang, Bobak Mortazavi |
| 2020 | ICLR | Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks. | Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin |
| 2020 | ISCA | SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation. | Yang Zhao, Xiaohan Chen, Yue Wang, Chaojian Li, Haoran You, Yonggan Fu, Yuan Xie, Zhangyang Wang, Yingyan Lin |
| 2019 | ICLR | ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA. | Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin |
| 2019 | ICML | Plug-and-Play Methods Provably Converge with Properly Trained Denoisers. | Ernest K. Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin |
| 2008 | CISS | On empirical capacity, random coding bound, and probability of outage of an object recognition system under constraint of PCA-encoding. | Xiaohan Chen, Natalia A. Schmid |
| 2007 | CISS | On Capacity of Automatic Target Recognition Systems Under the Constraint of PCA-Encoding. | Xiaohan Chen, Natalia A. Schmid |
| 2006 | ICIP | A Joint Shape-Intensity Estimation in Computerized Tomography in the Presence of High-Density Objects. | Xiaohan Chen, Natalia A. Schmid |
| 2006 | ICIP | On Performance Comparison of Real and Synthetic Iris Images. | Jinyu Zuo, Natalia A. Schmid, Xiaohan Chen |