Xiaohan Chen

According to our database, Xiaohan Chen authored at least 42 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee.

Haoyu Peter Wang, Jialin Liu, Xiaohan Chen, Xinshang Wang, Pan Li, Wotao Yin

Trans. Mach. Learn. Res., 2024

Expressive Power of Graph Neural Networks for (Mixed-Integer) Quadratic Programs.

Ziang Chen, Xiaohan Chen, Jialin Liu, Xinshang Wang, Wotao Yin

CoRR, 2024

Learning to optimize: A tutorial for continuous and mixed-integer optimization.

Xiaohan Chen, Jialin Liu, Wotao Yin

CoRR, 2024

Rethinking the Capacity of Graph Neural Networks for Branching Strategy.

Ziang Chen, Jialin Liu, Xiaohan Chen, Xinshang Wang, Wotao Yin

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

SmartDeal: Remodeling Deep Network Weights for Efficient Inference and Training.

Xiaohan Chen, Yang Zhao, Yue Wang, Pengfei Xu, Haoran You, Chaojian Li, Yonggan Fu, Yingyan Lin, Zhangyang Wang

IEEE Trans. Neural Networks Learn. Syst., October, 2023

Chasing Better Deep Image Priors between Over- and Under-parameterization.

Qiming Wu, Xiaohan Chen, Yifan Jiang, Zhangyang Wang

Trans. Mach. Learn. Res., 2023

DIG-MILP: a Deep Instance Generator for Mixed-Integer Linear Programming with Feasibility Guarantee.

Haoyu Wang, Jialin Liu, Xiaohan Chen, Xinshang Wang, Pan Li, Wotao Yin

CoRR, 2023

Towards Constituting Mathematical Structures for Learning to Optimize.

Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin, HanQin Cai

Proceedings of the International Conference on Machine Learning, 2023

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 Kärkkäinen, Mykola Pechenizkiy, Decebal Constantin Mocanu, Zhangyang Wang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Safeguarded Learned Convex Optimization.

Howard Heaton, Xiaohan Chen, Zhangyang Wang, Wotao Yin

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Learning to Optimize: A Primer and A Benchmark.

Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, Wotao Yin

J. Mach. Learn. Res., 2022

More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity.

Shiwei Liu, Tianlong Chen, Xiaohan Chen, Xuxi Chen, Qiao Xiao, Boqian Wu, Mykola Pechenizkiy, Decebal Constantin Mocanu, Zhangyang Wang

CoRR, 2022

Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets.

Ruisi Cai, Zhenyu Zhang, Tianlong Chen, Xiaohan Chen, Zhangyang Wang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

Model elasticity for hardware heterogeneity in federated learning systems.

Allen-Jasmin Farcas, Xiaohan Chen, Zhangyang Wang, Radu Marculescu

Proceedings of the 1st ACM Workshop on Data Privacy and Federated Learning Technologies for Mobile Edge Network, 2022

Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better.

Sameer Bibikar, Haris Vikalo, Zhangyang Wang, Xiaohan Chen

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity.

Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu

CoRR, 2021

SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training.

Xiaohan Chen, Yang Zhao, Yue Wang, Pengfei Xu, Haoran You, Chaojian Li, Yonggan Fu, Yingyan Lin, Zhangyang Wang

CoRR, 2021

Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?

Xiaolong Ma, Geng Yuan, Xuan Shen, Tianlong Chen, Xuxi Chen, Xiaohan Chen, Ning Liu, Minghai Qin, Sijia Liu, Zhangyang Wang, Yanzhi Wang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Sparse Training via Boosting Pruning Plasticity with Neuroregeneration.

Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Hyperparameter Tuning is All You Need for LISTA.

Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Elastic Lottery Ticket Hypothesis.

Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan, Jingjing Liu, Zhangyang Wang

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning A Minimax Optimizer: A Pilot Study.

Jiayi Shen, Xiaohan Chen, Howard Heaton, Tianlong Chen, Jialin Liu, Wotao Yin, Zhangyang Wang

Proceedings of the 9th International Conference on Learning Representations, 2021

A Design Space Study for LISTA and Beyond.

Tianjian Meng, Xiaohan Chen, Yifan Jiang, Zhangyang Wang

Proceedings of the 9th International Conference on Learning Representations, 2021

DynEHR: Dynamic adaptation of models with data heterogeneity in electronic health records.

Lida Zhang, Xiaohan Chen, Tianlong Chen, Zhangyang Wang, Bobak J. Mortazavi

Proceedings of the IEEE EMBS International Conference on Biomedical and Health Informatics, 2021

EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets.

Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan, Zhangyang Wang, Jingjing Liu

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020

ShiftAddNet: A Hardware-Inspired Deep Network.

Haoran You, Xiaohan Chen, Yongan Zhang, Chaojian Li, Sicheng Li, Zihao Liu, Zhangyang Wang, Yingyan Lin

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

MATE: Plugging in Model Awareness to Task Embedding for Meta Learning.

Xiaohan Chen, Zhangyang Wang, Siyu Tang, Krikamol Muandet

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Proceedings of the 47th ACM/IEEE Annual International Symposium on Computer Architecture, 2020

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

Proceedings of the 8th International Conference on Learning Representations, 2020

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

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

E2-Train: Energy-Efficient Deep Network Training with Data-, Model-, and Algorithm-Level Saving.

Yue Wang, Ziyu Jiang, Xiaohan Chen, Pengfei Xu, Yang Zhao, Yingyan Lin, Zhangyang Wang

CoRR, 2019

Drawing early-bird tickets: Towards more efficient training of deep networks.

Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Yingyan Lin, Zhangyang Wang, Richard G. Baraniuk

CoRR, 2019

E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings.

Yue Wang, Ziyu Jiang, Xiaohan Chen, Pengfei Xu, Yang Zhao, Yingyan Lin, Zhangyang Wang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Plug-and-Play Methods Provably Converge with Properly Trained Denoisers.

Ernest K. Ryu, Jialin Liu, Sicheng Wang, Xiaohan Chen, Zhangyang Wang, Wotao Yin

Proceedings of the 36th International Conference on Machine Learning, 2019

ALISTA: Analytic Weights Are As Good As Learned Weights in LISTA.

Jialin Liu, Xiaohan Chen, Zhangyang Wang, Wotao Yin

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?

Nitin Bansal, Xiaohan Chen, Zhangyang Wang

CoRR, 2018

Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds.

Xiaohan Chen, Jialin Liu, Zhangyang Wang, Wotao Yin

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Can We Gain More from Orthogonality Regularizations in Training Deep Networks?

Nitin Bansal, Xiaohan Chen, Zhangyang Wang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018