Biography
I am a Senior Research Scientist at Sea AI Lab (SAIL). I received my Ph.D. degree from TSAIL Group in the Department of Computer Science and Technology, Tsinghua University, advised by Prof. Jun Zhu. Before that, I received my B.S. degree of Mathematics and Physics from Tsinghua University in 2017. I collaborated with Prof. Stefano Ermon from February, 2020 to June, 2020 (online) in the Computer Science Department, Stanford University. I was a visiting student from July, 2016 to September, 2016 in the Computational Biology Department, Carnegie Mellon University, advised by Prof. Wei Wu.
My research interests span the areas of machine learning, including trustworthy machine learning and deep generative models. My research was supported by MSRA Fellowship and Baidu Scholarship.
My PhD Thesis (in Chinese) has been selected as CAAI Outstanding Doctoral Dissertation Award.
We are currently hiring Research Interns working on Trustworthy AI for Large Models. Please do not hesitate to contact me if you are interested and meet the minimum Requirements.
Publications
(* indicates equal contribution)
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Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses
Xiaosen Zheng, Tianyu Pang, Chao Du, Qian Liu, Jing Jiang, Min Lin
Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024
[code]
[arxiv]
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Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMs
Xuan Zhang, Chao Du, Tianyu Pang, Qian Liu, Wei Gao, Min Lin
Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024
[code]
[arxiv]
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Graph Diffusion Policy Optimization
Yijing Liu, Chao Du, Tianyu Pang, Chongxuan Li, Wei Chen, Min Lin
Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2024
[code]
[arxiv]
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Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast
Xiangming Gu*, Xiaosen Zheng*, Tianyu Pang*, Chao Du, Qian Liu, Ye Wang, Jing Jiang, Min Lin
International Conference on Machine Learning (ICML), Vienna, Austria, 2024
[code]
[arxiv]
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Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning
Zhaorui Yang, Tianyu Pang, Haozhe Feng, Han Wang, Wei Chen, Minfeng Zhu, Qian Liu
Annual Meeting of the Association for Computational Linguistics (ACL), Bangkok, Thailand, 2024
[code]
[arxiv]
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Finetuning Text-to-Image Diffusion Models for Fairness (Oral)
Xudong Shen, Chao Du, Tianyu Pang, Min Lin, Yongkang Wong, Mohan Kankanhalli
International Conference on Learning Representations (ICLR), Vienna, Austria, 2024
[code]
[arxiv]
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Intriguing Properties of Data Attribution on Diffusion Models
Xiaosen Zheng, Tianyu Pang, Chao Du, Jing Jiang, Min Lin
International Conference on Learning Representations (ICLR), Vienna, Austria, 2024
[code]
[arxiv]
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LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition
Chengsong Huang, Qian Liu, Bill Yuchen Lin, Tianyu Pang, Chao Du, Min Lin
Conference on Language Modeling (COLM), Philadelphia, USA, 2024
[code]
[arxiv]
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BAFFLE: A Baseline of Backpropagation-Free Federated Learning
Haozhe Feng, Tianyu Pang, Chao Du, Wei Chen, Shuicheng Yan, Min Lin
European Conference on Computer Vision (ECCV), Milano, Italy, 2024
[code]
[arxiv]
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Face3DAdv: Exploiting Robust Adversarial 3D Patches on Physical Face Recognition
Xiao Yang, Longlong Xu, Tianyu Pang, Yinpeng Dong, Yikai Wang, Hang Su, Jun Zhu
International Journal of Computer Vision (IJCV), 2024
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On Calibrating Diffusion Probabilistic Models
Tianyu Pang, Cheng Lu, Chao Du, Min Lin, Shuicheng Yan, Zhijie Deng
Annual Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023
[code]
[arxiv]
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On Evaluating Adversarial Robustness of Large Vision-Language Models
Yunqing Zhao*, Tianyu Pang*, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Cheung, Min Lin
Annual Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023
[code]
[arxiv]
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Efficient Diffusion Policies for Offline Reinforcement Learning
Bingyi Kang, Xiao Ma, Chao Du, Tianyu Pang, Shuicheng Yan
Annual Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023
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Gaussian Mixture Denoising Diffusion Probabilistic Models
Hanzhong Guo, Cheng Lu, Fan Bao, Tianyu Pang, Shuicheng Yan, Chao Du, Chongxuan Li
Annual Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2023
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Better Diffusion Models Further Improve Adversarial Training
Zekai Wang*, Tianyu Pang*, Chao Du, Min Lin, Weiwei Liu, Shuicheng Yan
International Conference on Machine Learning (ICML), Hawaii, USA, 2023
[code]
[poster]
[arxiv]
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Bag of Tricks for Training Data Extraction from Language Models
Weichen Yu, Tianyu Pang, Qian Liu, Chao Du, Bingyi Kang, Yan Huang, Min Lin, Shuicheng Yan
International Conference on Machine Learning (ICML), Hawaii, USA, 2023
[code]
[poster]
[arxiv]
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Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows
Chao Du, Tianbo Li, Tianyu Pang, Shuicheng Yan, Min Lin
International Conference on Machine Learning (ICML), Hawaii, USA, 2023
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Improving Adversarial Robustness of Deep Equilibrium Models with Explicit Regulations Along the Neural Dynamics
Zonghan Yang, Peng Li, Tianyu Pang, Yang Liu
International Conference on Machine Learning (ICML), Hawaii, USA, 2023
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Exploring Incompatible Knowledge Transfer in Few-shot Image Generation
Yunqing Zhao, Chao Du, Milad Abdollahzadeh, Tianyu Pang, Min Lin, Shuicheng Yan, Ngai-Man Cheung
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, 2023
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A Closer Look at the Adversarial Robustness of Deep Equilibrium Models
Zonghan Yang, Tianyu Pang, Yang Liu
Annual Conference on Neural Information Processing Systems (NeurIPS), New Orleans, USA, 2022
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Boosting Transferability of Targeted Adversarial Examples via Hierarchical Generative Networks
Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu
European Conference on Computer Vision (ECCV), Tel Aviv, Israel, 2022
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Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
Tianyu Pang, Min Lin, Xiao Yang, Jun Zhu, Shuicheng Yan
International Conference on Machine Learning (ICML), Baltimore, USA, 2022
[code]
[poster]
[video]
[arxiv]
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Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart
Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, USA, 2022
[code]
[poster]
[arxiv]
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Exploring Memorization in Adversarial Training
Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu
International Conference on Learning Representations (ICLR), Online, 2022
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Accumulative Poisoning Attacks on Real-time Data
Tianyu Pang*, Xiao Yang*, Yinpeng Dong, Hang Su, Jun Zhu
Annual Conference on Neural Information Processing Systems (NeurIPS), Online, 2021
[code]
[slide]
[arxiv]
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Towards Face Encryption by Generating Adversarial Identity Masks
Xiao Yang, Yinpeng Dong, Tianyu Pang, Jun Zhu, Hang Su
International Conference on Computer Vision (ICCV), Online, 2021
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Black-box Detection of Backdoor Attacks with Limited Information and Data
Yinpeng Dong, Xiao Yang, Zhijie Deng, Tianyu Pang, Zihao Xiao, Hang Su, and Jun Zhu
International Conference on Computer Vision (ICCV), Online, 2021
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Bag of Tricks for Adversarial Training
Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, Jun Zhu
International Conference on Learning Representations (ICLR), Online, 2021
[code]
[poster]
[arxiv]
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Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior
Yinpeng Dong, Shuyu Cheng, Tianyu Pang, Hang Su, and Jun Zhu
IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), 2021
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Efficient Learning of Generative Models via Finite-Difference Score Matching
Tianyu Pang*, Kun Xu*, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu
Annual Conference on Neural Information Processing Systems (NeurIPS), Online, 2020
[code]
[video]
[poster]
[arxiv]
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Boosting Adversarial Training with Hypersphere Embedding
Tianyu Pang*, Xiao Yang*, Yinpeng Dong, Kun Xu, Jun Zhu, Hang Su
Annual Conference on Neural Information Processing Systems (NeurIPS), Online, 2020
[code]
[video]
[poster]
[arxiv]
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Adversarial Distributional Training for Robust Deep Learning
Zhijie Deng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu
Annual Conference on Neural Information Processing Systems (NeurIPS), Online, 2020
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Benchmarking Adversarial Robustness on Image Classification (Oral)
Yinpeng Dong, Qi-An Fu, Xiao Yang, Tianyu Pang, Hang Su, Zihao Xiao, Jun Zhu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Online, 2020
[platform]
[slide]
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Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
Tianyu Pang, Kun Xu, Yinpeng Dong, Chao Du, Ning Chen, Jun Zhu
International Conference on Learning Representations (ICLR), Online, 2020
[code]
[video]
[slide]
[arxiv]
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Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks
Tianyu Pang*, Kun Xu*, Jun Zhu
International Conference on Learning Representations (ICLR), Online, 2020
[code]
[video]
[slide]
[arxiv]
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Improving Black-box Adversarial Attacks with a Transfer-based Prior
Shuyu Cheng, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu
Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019
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Improving Adversarial Robustness via Promoting Ensemble Diversity
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu
International Conference on Machine Learning (ICML), Long Beach, USA, 2019
[code]
[poster]
[slide]
[arxiv]
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Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks (Oral)
Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019
[code]
[video]
[poster]
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Towards Robust Detection of Adversarial Examples (Spotlight)
Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu
Annual Conference on Neural Information Processing Systems (NeurIPS), Montreal, Canada, 2018
[code]
[poster]
[slide]
[arxiv]
[video]
[press release]
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Max-Mahalanobis Linear Discriminant Analysis Networks
Tianyu Pang, Chao Du, Jun Zhu
International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018
[code]
[poster]
[slide]
[arxiv]
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Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
Fangzhou Liao, Ming Liang, Yinpeng Dong, Tianyu Pang, Jun Zhu, Xiaolin Hu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018
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Boosting Adversarial Attacks with Momentum (Spotlight)
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Xiaolin Hu, Jianguo Li, Jun Zhu
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018
Honors & Awards
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World's Top 2% Scientists, 2024.09
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WAIC Rising Star Award, 2023.07
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CAAI Outstanding Doctoral Dissertation Award, 2023.02
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Beijing Outstanding Graduates, 2022.06
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Yang Huiyan Scholarship, 2021.10
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Baidu Scholarship, 2020.12
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Microsoft Research Asia (MSRA) Fellowship, 2020.11
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Zhong Shimo Scholarship, 2020.11
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AI Chinese New Stars, 2020.11
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Shenzhen Stock Exchange Scholarship, 2020.10
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'84' Future Innovation Scholarship, 2019.12
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China National Scholarship, 2019.10
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NVIDIA Pioneering Research Award, 2018.12
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Schlumberger Scholarship, 2018.10
Competitions
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2023.01 --
IEEE SaTML 2023 Competition:
Training Data Extraction Challenge, 2nd place
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2020.10 --
Inclusion | A-tech Contest:
Part One, Part Two, 1st place of AI track
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2018.8 --
GeekPwn 2018 The Worldwide Cyber Security Contest:
CAAD CTF Las Vegas, 1st place
CAAD CTF Shanghai, 3rd place
CAAD Non-targeted Adversarial Attacks Track, 3rd place
CAAD Targeted Adversarial Attacks Track, 2nd place
CAAD Defense Against Adversarial Attack Track, 2nd place
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2017.10 --
NeurIPS 2017 Adversarial Attacks and Defense Competition:
Non-targeted Adversarial Attacks Track, 1st place
Targeted Adversarial Attacks, 1st place
Defense Against Adversarial Attack, 1st place
Services
I was an organizer of:
AAAI 2022 Workshop (Adversarial Machine Learning and Beyond)
[link]
ICML 2021 Workshop (A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning)
[link]
ICCV 2021 Workshop (Adversarial Robustness in the Real World)
[link]
I was a reviewer / PC member of conferences:
ICML 2019, 2020, 2021, 2022, 2023, 2024
ICLR 2020, 2021, 2022, 2023, 2024, 2025
NeurIPS 2019, 2020, 2021, 2022, 2023, 2024
AISTATS 2020, 2022, 2023, 2024
CVPR 2019, 2020, 2021, 2022, 2023, 2024
ICCV 2019, 2021, 2023
ECCV 2020, 2022, 2024
AAAI 2020
I was a reviewer of journals:
TPAMI, IJCV, TNNLS, TKDD, TIFS, ACM TOPS, SPL, MLJ, Neurocomputing
Invited Talks
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AI SIG Meetup, AiSP, 2024.8 [video]
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Technical Sharing Session, IMDA, 2024.6 [slides]
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CFAR Rising Star Seminar, A*STAR, 2023.12 [video][slides]
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Deep Learning and Optimization Seminar, Westlake University, 2023.11 [video][slides]
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VALSE Webinar, 2023.3 [video][slides]
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Xia Peisu Forum, ICT CAS, 2022.12 [slides]
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TechBeat, JiangMen, 2022.8 [video]
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TrustML Young Scientist Seminars, RIKEN, 2022.3 [video][slides]
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Alibaba Security, 2021.3 [video][slides]
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VALSE Webinar, 2020.12 [video][slides]
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RealCourse, RealAI, 2020.5 [video][slides]
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Graduate Research Seminars, Tsinghua University, 2019.12 [slides]
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CAAD CTF, DEFCON, 2018.8 [slides]
Teaching
2019 Fall, TA in Machine Learning, instructed by Prof. Jun Zhu
© 2024 Tianyu Pang