Can We Gain More from Orthogonality Regularizations in Training Deep Networks? - Nitin Bansal; Xiaohan Chen 001; Zhangyang Wang (NeurIPS, 2018)

Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds. - Xiaohan Chen 001; Jialin Liu 003; Zhangyang Wang; Wotao Yin (NeurIPS, 2018)

Can We Gain More from Orthogonality Regularizations in Training Deep CNNs? - Nitin Bansal; Xiaohan Chen 001; Zhangyang Wang (CoRR, 2018)

ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA. - Jialin Liu 003; Xiaohan Chen 001; Zhangyang Wang; Wotao Yin (ICLR (Poster), 2019)

Plug-and-Play Methods Provably Converge with Properly Trained Denoisers. - Ernest K. Ryu; Jialin Liu 003; Sicheng Wang 003; Xiaohan Chen 001; Zhangyang Wang; Wotao Yin (ICML, 2019)

E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings. - Yue Wang 036; Ziyu Jiang; Xiaohan Chen 001; Pengfei Xu 011; Yang Zhao 013; Yingyan Lin; Zhangyang Wang (NeurIPS, 2019)

Drawing early-bird tickets: Towards more efficient training of deep networks. - Haoran You; Chaojian Li; Pengfei Xu 011; Yonggan Fu; Yue Wang 036; Xiaohan Chen 001; Yingyan Lin; Zhangyang Wang; Richard G. Baraniuk (CoRR, 2019)

E2-Train: Energy-Efficient Deep Network Training with Data-, Model-, and Algorithm-Level Saving. - Yue Wang 036; Ziyu Jiang; Xiaohan Chen 001; Pengfei Xu 011; Yang Zhao 013; Yingyan Lin; Zhangyang Wang (CoRR, 2019)

Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery. - Zepeng Huo; Arash Pakbin; Xiaohan Chen 001; Nathan C. Hurley; Ye Yuan 012; Xiaoning Qian; Zhangyang Wang; Shuai Huang 001; Bobak Mortazavi (AISTATS, 2020)

Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks. - Haoran You; Chaojian Li; Pengfei Xu 011; Yonggan Fu; Yue Wang 036; Xiaohan Chen 001; Richard G. Baraniuk; Zhangyang Wang; Yingyan Lin (ICLR, 2020)

SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation. - Yang Zhao 013; Xiaohan Chen 001; Yue Wang 036; Chaojian Li; Haoran You; Yonggan Fu; Yuan Xie 001; Zhangyang Wang; Yingyan Lin (ISCA, 2020)

MATE: Plugging in Model Awareness to Task Embedding for Meta Learning. - Xiaohan Chen 001; Zhangyang Wang; Siyu Tang 001; Krikamol Muandet (NeurIPS, 2020)

ShiftAddNet: A Hardware-Inspired Deep Network. - Haoran You; Xiaohan Chen 001; Yongan Zhang; Chaojian Li; Sicheng Li 001; Zihao Liu; Zhangyang Wang; Yingyan Lin (NeurIPS, 2020)

EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets. - Xiaohan Chen 001; Yu Cheng 001; Shuohang Wang; Zhe Gan; Zhangyang Wang; Jingjing Liu 001 (ACL/IJCNLP (1), 2021)

DynEHR: Dynamic adaptation of models with data heterogeneity in electronic health records. - Lida Zhang; Xiaohan Chen 001; Tianlong Chen 001; Zhangyang Wang; Bobak J. Mortazavi (BHI, 2021)

A Design Space Study for LISTA and Beyond. - Tianjian Meng; Xiaohan Chen 001; Yifan Jiang 001; Zhangyang Wang (ICLR, 2021)

Learning A Minimax Optimizer: A Pilot Study. - Jiayi Shen; Xiaohan Chen 001; Howard Heaton; Tianlong Chen 001; Jialin Liu 003; Wotao Yin; Zhangyang Wang (ICLR, 2021)

The Elastic Lottery Ticket Hypothesis. - Xiaohan Chen 001; Yu Cheng 001; Shuohang Wang; Zhe Gan; Jingjing Liu 001; Zhangyang Wang (NeurIPS, 2021)

Hyperparameter Tuning is All You Need for LISTA. - Xiaohan Chen 001; Jialin Liu 003; Zhangyang Wang; Wotao Yin (NeurIPS, 2021)

Sparse Training via Boosting Pruning Plasticity with Neuroregeneration. - Shiwei Liu 003; Tianlong Chen 001; Xiaohan Chen 001; Zahra Atashgahi; Lu Yin 006; Huanyu Kou; Li Shen 008; Mykola Pechenizkiy; Zhangyang Wang; Decebal Constantin Mocanu (NeurIPS, 2021)

Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? - Xiaolong Ma; Geng Yuan; Xuan Shen; Tianlong Chen 001; Xuxi Chen; Xiaohan Chen 001; Ning Liu 007; Minghai Qin; Sijia Liu 001; Zhangyang Wang; Yanzhi Wang (NeurIPS, 2021)

SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training. - Xiaohan Chen 001; Yang Zhao 013; Yue Wang 036; Pengfei Xu 011; Haoran You; Chaojian Li; Yonggan Fu; Yingyan Lin; Zhangyang Wang (CoRR, 2021)

FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Zahra Atashgahi; Xiaohan Chen 001; Ghada Sokar; Elena Mocanu; Mykola Pechenizkiy; Zhangyang Wang; Decebal Constantin Mocanu (CoRR, 2021)

Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better. - Sameer Bibikar; Haris Vikalo; Zhangyang Wang; Xiaohan Chen 001 (AAAI, 2022)

Model elasticity for hardware heterogeneity in federated learning systems. - Allen-Jasmin Farcas; Xiaohan Chen 001; Zhangyang Wang; Radu Marculescu (FedEdge@MobiCom, 2022)

Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently. - Xiaohan Chen 001; Jason Zhang; Zhangyang Wang (ICLR, 2022)

Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Zahra Atashgahi; Xiaohan Chen 001; Ghada Sokar; Elena Mocanu; Mykola Pechenizkiy; Zhangyang Wang; Decebal Constantin Mocanu (ICLR, 2022)

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training. - Shiwei Liu 003; Tianlong Chen 001; Xiaohan Chen 001; Li Shen 008; Decebal Constantin Mocanu; Zhangyang Wang; Mykola Pechenizkiy (ICLR, 2022)

Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets. - Ruisi Cai; Zhenyu Zhang 015; Tianlong Chen 001; Xiaohan Chen 001; Zhangyang Wang (NeurIPS, 2022)

More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Xiaohan Chen 001; Xuxi Chen; Qiao Xiao; Boqian Wu; Mykola Pechenizkiy; Decebal Constantin Mocanu; Zhangyang Wang (CoRR, 2022)

Learning to Optimize: A Primer and A Benchmark. - Tianlong Chen 001; Xiaohan Chen 001; Wuyang Chen 001; Howard Heaton; Jialin Liu 003; Zhangyang Wang; Wotao Yin (J. Mach. Learn. Res., 2022)

Safeguarded Learned Convex Optimization. - Howard Heaton; Xiaohan Chen 001; Zhangyang Wang; Wotao Yin (AAAI, 2023)

Many-Task Federated Learning: A New Problem Setting and A Simple Baseline. - Ruisi Cai; Xiaohan Chen 001; Shiwei Liu 003; Jayanth Srinivasa; Myungjin Lee; Ramana Kompella; Zhangyang Wang (CVPR Workshops, 2023)

More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity. - Shiwei Liu 003; Tianlong Chen 001; Xiaohan Chen 001; Xuxi Chen; Qiao Xiao; Boqian Wu; Tommi Kärkkäinen; Mykola Pechenizkiy; Decebal Constantin Mocanu; Zhangyang Wang (ICLR, 2023)

Towards Constituting Mathematical Structures for Learning to Optimize. - Jialin Liu 003; Xiaohan Chen 001; Zhangyang Wang; Wotao Yin; HanQin Cai (ICML, 2023)

DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee. - Haoyu Wang 004; Jialin Liu 003; Xiaohan Chen 001; Xinshang Wang; Pan Li 005; Wotao Yin (CoRR, 2023)

Chasing Better Deep Image Priors between Over- and Under-parameterization. - Qiming Wu; Xiaohan Chen 001; Yifan Jiang 001; Zhangyang Wang (Trans. Mach. Learn. Res., 2023)

SmartDeal: Remodeling Deep Network Weights for Efficient Inference and Training. - Xiaohan Chen 001; Yang Zhao 013; Yue Wang 036; Pengfei Xu 011; Haoran You; Chaojian Li; Yonggan Fu; Yingyan Lin; Zhangyang Wang (IEEE Trans. Neural Networks Learn. Syst., 2023)

Rethinking the Capacity of Graph Neural Networks for Branching Strategy. - Ziang Chen; Jialin Liu 003; Xiaohan Chen 001; Xinshang Wang; Wotao Yin (NeurIPS, 2024)

Learning to optimize: A tutorial for continuous and mixed-integer optimization. - Xiaohan Chen 001; Jialin Liu 003; Wotao Yin (CoRR, 2024)

Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs. - Ziang Chen; Xiaohan Chen 001; Jialin Liu 003; Xinshang Wang; Wotao Yin (CoRR, 2024)

DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee. - Haoyu Peter Wang; Jialin Liu 003; Xiaohan Chen 001; Xinshang Wang; Pan Li 005; Wotao Yin (Trans. Mach. Learn. Res., 2024)