Junyuan (Jason) Hong

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Jiayu ZhouUniversity of MichiganVerified email at umich.edu
Zhangyang (Atlas) WangXTX Markets & University of Texas at AustinVerified email at utexas.edu
Zhuangdi ZhuGeorge Mason University; Michigan State UniversityVerified email at gmu.edu
Haotao WangQualcommVerified email at utexas.edu
Bo LiUniversity of Illinois at Urbana–ChampaignVerified email at illinois.edu
Hiroko H. DodgeMassachusetts General Hospital, Harvard Medical SchoolVerified email at mgh.harvard.edu
Dawn SongProfessor of Computer Science, UC BerkeleyVerified email at cs.berkeley.edu
Spranger MichaelSony AI / Sony Computer Science Laboratories Inc.Verified email at sony.com
Mehrdad MahdaviAssociate Professor of Computer Science, Pennsylvania State UniversityVerified email at psu.edu
Ruoxi JiaAssistant Professor, Virginia TechVerified email at vt.edu
Sijia LiuMichigan State University & MIT-IBM Watson AI LabVerified email at msu.edu
Tianlong ChenAssistant Professor, CS@UNC Chapel Hill; PostDoc, CSAIL@MIT+BMI@Harvard; Ph.D., ECE@UT AustinVerified email at cs.unc.edu
Mingyi HongAssociate Professor, University of Minnesota; Amazon ScholarVerified email at umn.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityVerified email at princeton.edu
Zhen XiangUniversity of GeorgiaVerified email at uga.edu
Kaidi XuAssistant Professor, Drexel UniversityVerified email at northeastern.edu
Bhavya KailkhuraResearch Scientist, Lawrence Livermore National LaboratoryVerified email at llnl.gov
Fei Wang, FAMIA, FIAHSI, FACMIProfessor at Cornell UniversityVerified email at med.cornell.edu
Ulisses Braga-NetoProfessor of Electrical and Computer Engineering, Texas A&M UniversityVerified email at ece.tamu.edu

Junyuan (Jason) Hong

Other namesJunyuan Hong

University of Texas at Austin

Verified email at utexas.edu - Homepage

Privacy Responsible AI AI4Health Federated Learning


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Data-free knowledge distillation for heterogeneous federated learning Z Zhu, J Hong, J Zhou International conference on machine learning, 12878-12889, 2021	685	2021
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization J Hong, H Wang, Z Wang, J Zhou International Conference on Learning Representations, 2022	53	2022
Federated adversarial debiasing for fair and transferable representations J Hong, Z Zhu, S Yu, Z Wang, HH Dodge, J Zhou Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021	53	2021
Federated robustness propagation: sharing adversarial robustness in heterogeneous federated learning J Hong, H Wang, Z Wang, J Zhou Proceedings of the AAAI Conference on Artificial Intelligence 37 (7), 7893-7901, 2023	48*	2023
MECTA: Memory-Economic Continual Test-Time Model Adaptation J Hong, L Lyu, J Zhou, M Spranger International Conference on Learning Representations, 2023	35	2023
Resilient and communication efficient learning for heterogeneous federated systems Z Zhu, J Hong, S Drew, J Zhou Proceedings of Thirty-ninth International Conference on Machine Learning …, 2022	31	2022
DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer J Hong, JT Wang, C Zhang, Z Li, B Li, Z Wang ICLR 2024, 2023	28*	2023
Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark Y Zhang, P Li, J Hong, J Li, Y Zhang, W Zheng, PY Chen, JD Lee, W Yin, ... ICML, 2024	24	2024
A privacy-preserving hybrid federated learning framework for financial crime detection H Zhang, J Hong, F Dong, S Drew, L Xue, J Zhou KDD International Workshop on Federated Learning for Distributed Data Mining, 2023	20	2023
Variant grassmann manifolds: A representation augmentation method for action recognition J Hong, Y Li, H Chen ACM Transactions on Knowledge Discovery from Data (TKDD) 13 (2), 1-23, 2019	14	2019
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection S Yu, J Hong, H Wang, Z Wang, J Zhou International Conference on Learning Representations, 2023	12	2023
How robust is your fairness? evaluating and sustaining fairness under unseen distribution shifts H Wang, J Hong, J Zhou, Z Wang Transactions on machine learning research 2023, 2023	12	2023
Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork H Wang, J Hong, A Zhang, J Zhou, Z Wang NeurIPS 2022, 2022	12	2022
Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent J Hong, Z Wang, J Zhou ACM Conference on Fairness, Accountability, and Transparency, 11-35, 2022	12	2022
Learning model-based privacy protection under budget constraints J Hong, H Wang, Z Wang, J Zhou Proceedings of the AAAI Conference on Artificial Intelligence 35 (9), 7702-7710, 2021	12	2021
Short sequence classification through discriminable linear dynamical system Y Li, J Hong, H Chen IEEE Transactions on neural networks and learning systems 30 (11), 3396-3408, 2019	12	2019
Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk Z Li, J Hong, B Li, Z Wang 2nd IEEE Conference on Secure and Trustworthy Machine Learning, 2024	11	2024
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression J Hong, J Duan, C Zhang, Z Li, C Xie, K Lieberman, J Diffenderfer, ... ICML, 2024	10	2024
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling J Hong, L Lyu, J Zhou, M Spranger Advances in Neural Information Processing Systems, 2022	9	2022
Disturbance Grassmann kernels for subspace-based learning J Hong, H Chen, F Lin Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018	9	2018

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