default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDYonggang Zhang 0003
Person information
- affiliation: Hong Kong Baptist University, Department of Computer Science, Hong Kong
Other persons with the same name
- Yonggang Zhang — disambiguation page
- Yonggang Zhang 0001
![[0000-0003-4548-1111]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0003-4548-1111")
— Harbin Engineering University, College of Intelligent Systems Science and Engineering, China (and 2 more) - Yonggang Zhang 0002 — Jilin University, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering, Changchun, China
- Yonggang Zhang 0004 — University of Kassel, Department of Energy Management and Power System Operation, Germany (and 1 more)
- Yonggang Zhang 0005 — Huawei Corporation, HiSilicon, Department of Connectivity, Shanghai, China (and 1 more)
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2025
[j1]Xuhui Li, Zhen Fang, Yonggang Zhang, Ning Ma, Jiajun Bu, Bo Han, Haishuai Wang:
Characterizing Submanifold Region for Out-of-Distribution Detection. IEEE Trans. Knowl. Data Eng. 37(1): 130-147 (2025)- 2024
- [c21]Yang Lu, Lin Chen, Yonggang Zhang, Yiliang Zhang, Bo Han, Yiu-ming Cheung, Hanzi Wang:
Federated Learning with Extremely Noisy Clients via Negative Distillation. AAAI 2024: 14184-14192 - [c20]Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han:
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. ICLR 2024 - [c19]Jun Nie, Yonggang Zhang, Zhen Fang, Tongliang Liu, Bo Han, Xinmei Tian:
Out-of-Distribution Detection with Negative Prompts. ICLR 2024 - [c18]Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian, Tongliang Liu, Bo Han, Xiaowen Chu:
FedImpro: Measuring and Improving Client Update in Federated Learning. ICLR 2024 - [c17]Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han:
Robust Training of Federated Models with Extremely Label Deficiency. ICLR 2024 - [c16]Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han:
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. ICLR 2024 - [c15]Wei Zhang, Chaoqun Wan, Yonggang Zhang, Yiu-ming Cheung, Xinmei Tian, Xu Shen, Jieping Ye:
Interpreting and Improving Large Language Models in Arithmetic Calculation. ICML 2024 - [c14]Zhikai Hu, Yiu-ming Cheung, Yonggang Zhang, Peiying Zhang, Pui-ling Tang:
Component-Level Oracle Bone Inscription Retrieval. ICMR 2024: 647-656 - [i21]Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian, Tongliang Liu, Bo Han, Xiaowen Chu:
FedImpro: Measuring and Improving Client Update in Federated Learning. CoRR abs/2402.07011 (2024) - [i20]Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han:
Robust Training of Federated Models with Extremely Label Deficiency. CoRR abs/2402.14430 (2024) - [i19]Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han:
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. CoRR abs/2402.15070 (2024) - [i18]Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han:
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. CoRR abs/2403.08840 (2024) - [i17]Wei Zhang, Chaoqun Wan, Yonggang Zhang, Yiu-ming Cheung, Xinmei Tian, Xu Shen, Jieping Ye:
Interpreting and Improving Large Language Models in Arithmetic Calculation. CoRR abs/2409.01659 (2024) - [i16]Yu Zheng, Wenchao Zhang, Yonggang Zhang, Wei Song, Kai Zhou, Bo Han:
Rethinking Improved Privacy-Utility Trade-off with Pre-existing Knowledge for DP Training. CoRR abs/2409.03344 (2024) - [i15]Zhenheng Tang, Yonggang Zhang, Peijie Dong, Yiu-ming Cheung, Amelie Chi Zhou, Bo Han, Xiaowen Chu:
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion. CoRR abs/2410.20380 (2024) - [i14]Jun Nie, Yonggang Zhang, Tongliang Liu, Yiu-ming Cheung, Bo Han, Xinmei Tian:
Detecting Discrepancies Between AI-Generated and Natural Images Using Uncertainty. CoRR abs/2412.05897 (2024) - 2023
- [c13]Huantong Li, Xiangmiao Wu, Fanbing Lv, Daihai Liao, Thomas H. Li, Yonggang Zhang, Bo Han, Mingkui Tan:
Hard Sample Matters a Lot in Zero-Shot Quantization. CVPR 2023: 24417-24426 - [c12]Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, Kaili Ma, Han Yang, Peilin Zhao, Bo Han, James Cheng:
Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization. ICLR 2023 - [c11]Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian:
Moderately Distributional Exploration for Domain Generalization. ICML 2023: 6786-6817 - [c10]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-distribution Detection. NeurIPS 2023 - [c9]Zige Wang, Yonggang Zhang, Zhen Fang, Long Lan, Wenjing Yang, Bo Han:
SODA: Robust Training of Test-Time Data Adaptors. NeurIPS 2023 - [c8]Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, Bo Han:
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning. NeurIPS 2023 - [i13]Huantong Li, Xiangmiao Wu, Fanbing Lv, Daihai Liao, Thomas H. Li, Yonggang Zhang, Bo Han, Mingkui Tan:
Hard Sample Matters a Lot in Zero-Shot Quantization. CoRR abs/2303.13826 (2023) - [i12]Rui Dai, Yonggang Zhang, Zhen Fang, Bo Han, Xinmei Tian:
Moderately Distributional Exploration for Domain Generalization. CoRR abs/2304.13976 (2023) - [i11]Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, Bo Han:
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning. CoRR abs/2310.05077 (2023) - [i10]Zige Wang, Yonggang Zhang, Zhen Fang, Long Lan, Wenjing Yang, Bo Han:
SODA: Robust Training of Test-Time Data Adaptors. CoRR abs/2310.11093 (2023) - [i9]Qizhou Wang, Zhen Fang, Yonggang Zhang, Feng Liu, Yixuan Li, Bo Han:
Learning to Augment Distributions for Out-of-Distribution Detection. CoRR abs/2311.01796 (2023) - [i8]Yang Lu, Lin Chen, Yonggang Zhang, Yiliang Zhang, Bo Han, Yiu-ming Cheung, Hanzi Wang:
Federated Learning with Extremely Noisy Clients via Negative Distillation. CoRR abs/2312.12703 (2023) - 2022
- [c7]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. ICLR 2022 - [c6]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness Through the Lens of Causality. ICLR 2022 - [c5]Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xin He, Bo Han, Xiaowen Chu:
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. ICML 2022: 21111-21132 - [c4]Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs. NeurIPS 2022 - [c3]Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian:
Towards Lightweight Black-Box Attack Against Deep Neural Networks. NeurIPS 2022 - [c2]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. NeurIPS 2022 - [i7]Yongqiang Chen, Yonggang Zhang, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, James Cheng:
Invariance Principle Meets Out-of-Distribution Generalization on Graphs. CoRR abs/2202.05441 (2022) - [i6]Yongqiang Chen, Han Yang, Yonggang Zhang, Kaili Ma, Tongliang Liu, Bo Han, James Cheng:
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability. CoRR abs/2202.08057 (2022) - [i5]Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xin He, Bo Han, Xiaowen Chu:
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. CoRR abs/2206.02465 (2022) - [i4]Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Kaili Ma, Yonggang Zhang, Han Yang, Bo Han, James Cheng:
Pareto Invariant Risk Minimization. CoRR abs/2206.07766 (2022) - [i3]Chenghao Sun, Yonggang Zhang, Chaoqun Wan, Qizhou Wang, Ya Li, Tongliang Liu, Bo Han, Xinmei Tian:
Towards Lightweight Black-Box Attacks against Deep Neural Networks. CoRR abs/2209.14826 (2022) - [i2]Qizhou Wang, Feng Liu, Yonggang Zhang, Jing Zhang, Chen Gong, Tongliang Liu, Bo Han:
Watermarking for Out-of-distribution Detection. CoRR abs/2210.15198 (2022) - 2021
- [i1]Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang:
Adversarial Robustness through the Lens of Causality. CoRR abs/2106.06196 (2021) - 2020
- [c1]Yonggang Zhang, Ya Li, Tongliang Liu, Xinmei Tian:
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks. ICML 2020: 11163-11172
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-01-30 21:35 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
