Publications

• 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)
• 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 Oral) [NeurIPS 2023 (Datasets and Benchmarks Track)] [arXiv] [website] [blog] [code] (*Lead authors)
• Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding Xuefei Ning*, Zinan Lin*, Zixuan Zhou*, Zifu Wang, Huazhong Yang, Yu Wang [NeurIPS 2023 Workshop on Efficient Natural Language and Speech Processing] [arXiv] [website] [blog] [code] (*Equal contribution)
• Differentially Private Synthetic Data via Foundation Model APIs 1: Images Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin (NeurIPS Workshop Oral) [NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI] [arXiv] [code]
• 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]
• 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]
• 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 [arXiv] [code]

• 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)]
• 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)]

• 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]
• MLGO: a Machine Learning Guided Compiler Optimizations Framework Mircea Trofin*, Yundi Qian*, Eugene Brevdo, Zinan Lin, Krzysztof Choromanski, David Li [arXiv] [code] (*Equal contribution)

• 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]

• Towards Oblivious Network Analysis using Generative Adversarial Networks Zinan Lin, Soo-Jin Moon, Carolina M. Zarate, Ritika Mulagalapalli, Sekar Kulandaivel, Giulia Fanti, Vyas Sekar [HotNets 2019] [talk]

• PacGAN: The Power of Two Samples in Generative Adversarial Networks Zinan Lin, Ashish Khetan, Giulia Fanti, Sewoong Oh [NeurIPS 2018] [JSAIT 2020] [arXiv] [poster] [website] [interview] [code] [invited talk at yanxishe.com (in Chinese)]
• Robustness of Conditional GANs to Noisy Labels Kiran Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh (NeurIPS Spotlight) [NeurIPS 2018] [arXiv] [code]
• RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network Zinan Lin, Yongfeng Huang, Jilong Wang [IEEE Transactions on Information Forensics and Security] [PDF (3.5MB)] [code]