Zinan Lin's homepage
Publications
- Last updated: April 9, 2024
Publications
2024
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Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better
Enshu Liu*,
Junyi Zhu*,
Zinan Lin+‡,
Xuefei Ning+‡,
Matthew B. Blaschko,
Sergey Yekhanin,
Shengen Yan,
Guohao Dai,
Huazhong Yang,
Yu Wang‡
[arXiv]
[code]
(*Equal contribution)
(+Co-advise)
(‡Corresponding authors)
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Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation
[Previous Title] Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding
Xuefei Ning*,
Zinan Lin*,
Zixuan Zhou*,
Zifu Wang,
Huazhong Yang,
Yu Wang
[ICLR 2024]
[NeurIPS 2023 Workshop on Efficient Natural Language and Speech Processing]
[arXiv]
[website]
[blog]
[code]
(*Equal contribution)
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Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Zinan Lin,
Sivakanth Gopi,
Janardhan Kulkarni,
Harsha Nori,
Sergey Yekhanin
(NeurIPS Workshop Oral)
[ICLR 2024]
[NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI]
[arXiv]
[code]
[invited talk at AI Time (in Chinese)]
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Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie,
Zinan Lin,
Arturs Backurs,
Sivakanth Gopi,
Da Yu,
Huseyin Inan,
Harsha Nori,
Haotian Jiang,
Huishuai Zhang,
Yin Tat Lee,
Bo Li,
Sergey Yekhanin
[ICLR 2024 Workshop on Secure and Trustworthy Large Language Models]
[arXiv]
[code]
[website]
-
Statistic Maximal Leakage
Shuaiqi Wang,
Zinan Lin,
Giulia Fanti
[ISIT 2024]
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Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang,
Richard Shin,
Huseyin A. Inan,
Andre Manoel,
Fatemehsadat Mireshghallah,
Zinan Lin,
Sivakanth Gopi,
Janardhan Kulkarni,
Robert Sim
[ICLR 2024]
[arXiv]
[code]
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Efficiently Computing Similarities to Private Datasets
Arturs Backurs,
Zinan Lin,
Sepideh Mahabadi,
Sandeep Silwal,
Jakub Tarnawski
[ICLR 2024]
[arXiv]
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Rescaling Intermediate Features Makes Trained Consistency Models Perform Better
Junyi Zhu,
Zinan Lin,
Enshu Liu,
Xuefei Ning,
Matthew B. Blaschko
[ICLR 2024 (TinyPapers)]
-
Mixture-of-Linear-Experts for Long-term Time Series Forecasting
Ronghao Ni,
Zinan Lin,
Shuaiqi Wang,
Giulia Fanti
[AISTATS 2024]
[arXiv]
-
FlashEval: Towards Fast and Accurate Evaluation of Text-to-image Diffusion Generative Models
Lin Zhao*,
Tianchen Zhao*,
Zinan Lin,
Xuefei Ning,
Guohao Dai,
Huazhong Yang,
Yu Wang
[CVPR 2024]
[arXiv]
(*Equal contribution)
2023
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OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models
Enshu Liu*,
Xuefei Ning*,
Zinan Lin*,
Huazhong Yang,
Yu Wang
[ICML 2023]
[arXiv]
[code]
[website]
(*Equal contribution)
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DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang*,
Weixin Chen*,
Hengzhi Pei*,
Chulin Xie*,
Mintong Kang*,
Chenhui Zhang*,
Chejian Xu,
Zidi Xiong,
Ritik Dutta,
Rylan Schaeffer,
Sang T Truong,
Simran Arora,
Mantas Mazeika,
Dan Hendrycks,
Zinan Lin,
Yu Cheng,
Sanmi Koyejo,
Dawn Song,
Bo Li*
(NeurIPS Outstanding Paper & Oral)
[NeurIPS 2023]
[arXiv]
[website]
[blog]
[code]
(*Lead authors)
-
Training Private and Efficient Language Models with Synthetic Data from LLMs
Da Yu,
Arturs Backurs,
Sivakanth Gopi,
Huseyin Inan,
Janardhan Kulkarni,
Zinan Lin,
Chulin Xie,
Huishuai Zhang,
Wanrong Zhang
[NeurIPS 2023 Workshop on Socially Responsible Language Modelling Research]
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Summary Statistic Privacy in Data Sharing
Zinan Lin*,
Shuaiqi Wang*,
Vyas Sekar,
Giulia Fanti
[arXiv]
(*Equal contribution)
-
Selective Pre-training for Private Fine-tuning
Da Yu,
Sivakanth Gopi,
Janardhan Kulkarni,
Zinan Lin,
Saurabh Naik,
Tomasz Lukasz Religa,
Jian Yin,
Huishuai Zhang
[arXiv]
2022
-
Distributional Privacy for Data Sharing
Zinan Lin*,
Shuaiqi Wang*,
Vyas Sekar,
Giulia Fanti
[NeurIPS 2022 Workshop SyntheticData4ML]
(*Equal contribution)
-
Practical GAN-based Synthetic IP Header Trace Generation using NetShare
Yucheng Yin,
Zinan Lin,
Minhao Jin,
Giulia Fanti,
Vyas Sekar
[SIGCOMM 2022]
[code]
[talk]
-
RareGAN: Generating Samples for Rare Classes
Zinan Lin,
Hao Liang,
Giulia Fanti,
Vyas Sekar
[AAAI 2022]
[arXiv]
[code]
[talk & slide]
[invited blog at AI Time (in Chinese)]
[invited talk at AI Time (in Chinese)]
-
PcapShare: Exploring the Feasibility of GANs for Synthetic Packet Header Trace Generation
Yucheng Yin,
Zinan Lin,
Minhao Jin,
Giulia Fanti,
Vyas Sekar
[COMSNETS 2022 (demo)]
2021
-
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements
Zinan Lin,
Vyas Sekar,
Giulia Fanti
[NeurIPS 2021]
[arXiv]
[code]
[talk & slide]
[invited talk at AI Time (in Chinese)]
[blog]
-
On the Privacy Properties of GAN-generated Samples
Zinan Lin,
Vyas Sekar,
Giulia Fanti
[AISTATS 2021]
[arXiv]
[talk & slide]
-
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
Todd Huster,
Jeremy E.J. Cohen,
Zinan Lin,
Kevin Chan,
Charles Kamhoua,
Nandi Leslie,
Cho-Yu Jason Chiang,
Vyas Sekar
[ICML 2021]
[arXiv]
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MLGO: a Machine Learning Guided Compiler Optimizations Framework
Mircea Trofin*,
Yundi Qian*,
Eugene Brevdo,
Zinan Lin,
Krzysztof Choromanski,
David Li
[arXiv]
[code]
(*Equal contribution)
2020
-
Using GANs for Sharing Networked Timeseries Data: Challenges, Initial Promise, and Open Questions
[Previous Title] Generating High-fidelity, Synthetic Time Series Datasets with DoppelGANger
Zinan Lin,
Alankar Jain,
Chen Wang,
Giulia Fanti,
Vyas Sekar
(IMC Best Paper Finalist)
[IMC 2020]
[arXiv]
[code]
[talk]
[blog]
-
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
[Previous Title] InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers
Zinan Lin,
Kiran Thekumparampil,
Giulia Fanti,
Sewoong Oh
[ICML 2020]
[arXiv]
[code]
[talk & slide]
2019
2018
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