Zinan Lin's homepage
Publications
- Last updated: December 13, 2024
Publications
2024
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Can LLMs Learn by Teaching for Better Reasoning? A Preliminary Study
[Previous Title] Can LLMs Learn by Teaching? A Preliminary Study
Xuefei Ning*‡,
Zifu Wang*,
Shiyao Li*,
Zinan Lin*‡,
Peiran Yao*,
Tianyu Fu,
Matthew B Blaschko,
Guohao Dai,
Huazhong Yang,
Yu Wang‡
[NeurIPS 2024]
[arXiv]
[website]
[code]
[blog]
(*Equal contribution)
(‡Corresponding authors)
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Improving the Training of Rectified Flows
Sangyun Lee,
Zinan Lin,
Giulia Fanti
[NeurIPS 2024]
[arXiv]
[code]
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RedCode: Risky Code Execution and Generation Benchmark for Code Agents
Chengquan Guo*,
Xun Liu*,
Chulin Xie*,
Andy Zhou,
Yi Zeng,
Zinan Lin,
Dawn Song,
Bo Li
[NeurIPS 2024]
[arXiv]
[code]
(*Equal contribution)
-
GenMAC: Compositional Text-to-Video Generation with Multi-Agent Collaboration
Kaiyi Huang,
Yukun Huang,
Xuefei Ning,
Zinan Lin,
Yu Wang,
Xihui Liu
[arXiv]
[website]
[code]
[video]
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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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Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs
Enshu Liu*,
Junyi Zhu*,
Zinan Lin+‡,
Xuefei Ning+‡,
Matthew B. Blaschko,
Shengen Yan,
Guohao Dai,
Huazhong Yang,
Yu Wang‡
[arXiv]
[code]
(*Equal contribution)
(+Co-advise)
(‡Corresponding authors)
-
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]
[blog]
[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
(ICML Spotlight)
[ICML 2024]
[ICLR 2024 Workshop on Secure and Trustworthy Large Language Models]
[arXiv]
[code]
[website]
[blog]
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Statistic Maximal Leakage
Shuaiqi Wang,
Zinan Lin,
Giulia Fanti
[ISIT 2024]
[arXiv]
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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]
[blog]
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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)]
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Summary Statistic Privacy in Data Sharing
Zinan Lin*,
Shuaiqi Wang*,
Vyas Sekar,
Giulia Fanti
[JSAIT 2024]
[arXiv]
(*Equal contribution)
-
Mixture-of-Linear-Experts for Long-term Time Series Forecasting
Ronghao Ni,
Zinan Lin,
Shuaiqi Wang,
Giulia Fanti
[AISTATS 2024]
[arXiv]
[code]
[blog]
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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)
-
Selective Pre-training for Private Fine-tuning
Da Yu,
Sivakanth Gopi,
Janardhan Kulkarni,
Zinan Lin,
Saurabh Naik,
Tomasz Lukasz Religa,
Jian Yin,
Huishuai Zhang
[TMLR]
[arXiv]
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MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization
Tianchen Zhao*,
Xuefei Ning*,
Tongcheng Fang*,
Enshu Liu,
Guyue Huang,
Zinan Lin,
Shengen Yan,
Guohao Dai,
Yu Wang
[ECCV 2024]
[arXiv]
[code]
[Hugging Face Pipeline]
[website]
(*Equal contribution)
-
ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation
Tianchen Zhao,
Tongcheng Fang,
Enshu Liu,
Wan Rui,
Widyadewi Soedarmadji,
Shiyao Li,
Zinan Lin,
Guohao Dai,
Shengen Yan,
Huazhong Yang,
Xuefei Ning,
Yu Wang
[arXiv]
[code]
[website]
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Inferentially-Private Private Information
Shuaiqi Wang,
Shuran Zheng,
Zinan Lin,
Giulia Fanti,
Zhiwei Steven Wu
[arXiv]
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]
2022
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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]
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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)]
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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
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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]
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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
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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]
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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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