Zinan Lin

According to our database, Zinan Lin authored at least 35 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Selective Pre-training for Private Fine-tuning.

Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang

Trans. Mach. Learn. Res., 2024

Summary Statistic Privacy in Data Sharing.

Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti

IEEE J. Sel. Areas Inf. Theory, 2024

Inferentially-Private Private Information.

Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu

CoRR, 2024

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

CoRR, 2024

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

CoRR, 2024

ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation.

Tianchen Zhao, Tongcheng Fang, Enshu Liu, Rui Wan, Widyadewi Soedarmadji, Shiyao Li, Zinan Lin, Guohao Dai, Shengen Yan, Huazhong Yang, Xuefei Ning, Yu Wang

CoRR, 2024

Improving the Training of Rectified Flows.

Sangyun Lee, Zinan Lin, Giulia Fanti

CoRR, 2024

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

CoRR, 2024

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

CoRR, 2024

Statistic Maximal Leakage.

Shuaiqi Wang, Zinan Lin, Giulia Fanti

Proceedings of the IEEE International Symposium on Information Theory, 2024

Differentially Private Synthetic Data via Foundation Model APIs 2: Text.

Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin A. Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin

Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation.

Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, Huazhong Yang, Yu Wang

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Efficiently Computing Similarities to Private Datasets.

Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Rescaling Intermediate Features Makes Trained Consistency Models Perform Better.

Junyi Zhu, Zinan Lin, Enshu Liu, Xuefei Ning, Matthew B. Blaschko

Proceedings of the Second Tiny Papers Track at ICLR 2024, 2024

Differentially Private Synthetic Data via Foundation Model APIs 1: Images.

Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Mixture-of-Linear-Experts for Long-term Time Series Forecasting.

Ronghao Ni, Zinan Lin, Shuaiqi Wang, Giulia Fanti

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding.

Xuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, Yu Wang

CoRR, 2023

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models.

Enshu Liu, Xuefei Ning, Zinan Lin, Huazhong Yang, Yu Wang

Proceedings of the International Conference on Machine Learning, 2023

2022

Practical GAN-based synthetic IP header trace generation using NetShare.

Yucheng Yin, Zinan Lin, Minhao Jin, Giulia Fanti, Vyas Sekar

Proceedings of the SIGCOMM '22: ACM SIGCOMM 2022 Conference, Amsterdam, The Netherlands, August 22, 2022

RareGAN: Generating Samples for Rare Classes.

Zinan Lin, Hao Liang, Giulia Fanti, Vyas Sekar

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

MLGO: a Machine Learning Guided Compiler Optimizations Framework.

Mircea Trofin, Yundi Qian, Eugene Brevdo, Zinan Lin, Krzysztof Choromanski, David Li

CoRR, 2021

Why Spectral Normalization Stabilizes GANs: Analysis and Improvements.

Zinan Lin, Vyas Sekar, Giulia Fanti

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions.

Todd Huster, Jeremy E. J. Cohen, Zinan Lin, Kevin Chan, Charles A. Kamhoua, Nandi O. Leslie, Cho-Yu Jason Chiang, Vyas Sekar

Proceedings of the 38th International Conference on Machine Learning, 2021

On the Privacy Properties of GAN-generated Samples.

Zinan Lin, Vyas Sekar, Giulia Fanti

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

PacGAN: The Power of Two Samples in Generative Adversarial Networks.

Zinan Lin, Ashish Khetan, Giulia Fanti, Sewoong Oh

IEEE J. Sel. Areas Inf. Theory, 2020

Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions.

Zinan Lin, Alankar Jain, Chen Wang, Giulia Fanti, Vyas Sekar

Proceedings of the IMC '20: ACM Internet Measurement Conference, 2020

InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs.

Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Generating High-fidelity, Synthetic Time Series Datasets with DoppelGANger.

Zinan Lin, Alankar Jain, Chen Wang, Giulia Fanti, Vyas Sekar

CoRR, 2019

InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers.

Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh

CoRR, 2019

Towards Oblivious Network Analysis using Generative Adversarial Networks.

Zinan Lin, Soo-Jin Moon, Carolina M. Zarate, Ritika Mulagalapalli, Sekar Kulandaivel, Giulia Fanti, Vyas Sekar

Proceedings of the 18th ACM Workshop on Hot Topics in Networks, 2019

2018

RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network.

Zinan Lin, Yongfeng Huang, Jilong Wang

IEEE Trans. Inf. Forensics Secur., 2018

Robustness of conditional GANs to noisy labels.

Kiran Koshy Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018