Zinan Lin

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 45

Top Topics

Differentially Private

Diffusion Models

Representational Power

Top Venues

Publications

Enshu Liu, Junyi Zhu, Zinan Lin, Xuefei Ning, Matthew B. Blaschko, Shengen Yan, Guohao Dai, Huazhong Yang, Yu Wang
Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs. CoRR (2024)
Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
Summary Statistic Privacy in Data Sharing. IEEE J. Sel. Areas Inf. Theory 5 (2024)
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
Differentially Private Synthetic Data via Foundation Model APIs 2: Text. CoRR (2024)
Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski
Efficiently Computing Similarities to Private Datasets. ICLR (2024)
Tianchen Zhao, Xuefei Ning, Tongcheng Fang, Enshu Liu, Guyue Huang, Zinan Lin, Shengen Yan, Guohao Dai, Yu Wang
MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization. CoRR (2024)
Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
Selective Pre-training for Private Fine-tuning. Trans. Mach. Learn. Res. 2024 (2024)
Xinyu Tang, Richard Shin, Huseyin A. Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Robert Sim
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation. ICLR (2024)
Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin
Differentially Private Synthetic Data via Foundation Model APIs 1: Images. ICLR (2024)
Sangyun Lee, Zinan Lin, Giulia Fanti
Improving the Training of Rectified Flows. CoRR (2024)
Lin Zhao, Tianchen Zhao, Zinan Lin, Xuefei Ning, Guohao Dai, Huazhong Yang, Yu Wang
FlashEval: Towards Fast and Accurate Evaluation of Text-to-image Diffusion Generative Models. CoRR (2024)
Xuefei Ning, Zifu Wang, Shiyao Li, Zinan Lin, Peiran Yao, Tianyu Fu, Matthew B. Blaschko, Guohao Dai, Huazhong Yang, Yu Wang
Can LLMs Learn by Teaching? A Preliminary Study. CoRR (2024)
Tianchen Zhao, Tongcheng Fang, Enshu Liu, Rui Wan, Widyadewi Soedarmadji, Shiyao Li, Zinan Lin, Guohao Dai, Shengen Yan, Huazhong Yang, Xuefei Ning, Yu Wang
ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation. CoRR (2024)
Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, Huazhong Yang, Yu Wang
Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation. ICLR (2024)
Enshu Liu, Junyi Zhu, Zinan Lin, Xuefei Ning, Matthew B. Blaschko, Sergey Yekhanin, Shengen Yan, Guohao Dai, Huazhong Yang, Yu Wang
Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better. CoRR (2024)
Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski
Efficiently Computing Similarities to Private Datasets. CoRR (2024)
Junyi Zhu, Zinan Lin, Enshu Liu, Xuefei Ning, Matthew B. Blaschko
Rescaling Intermediate Features Makes Trained Consistency Models Perform Better. Tiny Papers @ ICLR (2024)
Ronghao Ni, Zinan Lin, Shuaiqi Wang, Giulia Fanti
Mixture-of-Linear-Experts for Long-term Time Series Forecasting. AISTATS (2024)
Ronghao Ni, Zinan Lin, Shuaiqi Wang, Giulia Fanti
Mixture-of-Linear-Experts for Long-term Time Series Forecasting. CoRR (2023)
Enshu Liu, Xuefei Ning, Zinan Lin, Huazhong Yang, Yu Wang
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models. ICML (2023)
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
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models. NeurIPS (2023)
Xinyu Tang, Richard Shin, Huseyin A. Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Robert Sim
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation. CoRR (2023)
Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
Summary Statistic Privacy in Data Sharing. CoRR (2023)
Enshu Liu, Xuefei Ning, Zinan Lin, Huazhong Yang, Yu Wang
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models. CoRR (2023)
Xuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, Yu Wang
Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding. CoRR (2023)
Zinan Lin, Vyas Sekar, Giulia Fanti
On the Privacy Properties of GAN-generated Samples. CoRR (2022)
Zinan Lin, Hao Liang, Giulia Fanti, Vyas Sekar
RareGAN: Generating Samples for Rare Classes. AAAI (2022)
Zinan Lin, Hao Liang, Giulia Fanti, Vyas Sekar
RareGAN: Generating Samples for Rare Classes. CoRR (2022)
Yucheng Yin, Zinan Lin, Minhao Jin, Giulia Fanti, Vyas Sekar
Practical GAN-based synthetic IP header trace generation using NetShare. SIGCOMM (2022)
Mircea Trofin, Yundi Qian, Eugene Brevdo, Zinan Lin, Krzysztof Choromanski, David Li
MLGO: a Machine Learning Guided Compiler Optimizations Framework. CoRR (2021)
Todd Huster, Jeremy E. J. Cohen, Zinan Lin, Kevin Chan, Charles A. Kamhoua, Nandi Leslie, Cho-Yu Jason Chiang, Vyas Sekar
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions. CoRR (2021)
Todd Huster, Jeremy E. J. Cohen, Zinan Lin, Kevin Chan, Charles A. Kamhoua, Nandi O. Leslie, Cho-Yu Jason Chiang, Vyas Sekar
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions. ICML (2021)
Zinan Lin, Vyas Sekar, Giulia C. Fanti
On the Privacy Properties of GAN-generated Samples. AISTATS (2021)
Zinan Lin, Vyas Sekar, Giulia Fanti
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements. NeurIPS (2021)
Zinan Lin, Vyas Sekar, Giulia C. Fanti
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements. CoRR (2020)
Zinan Lin, Kiran Koshy Thekumparampil, Giulia C. Fanti, Sewoong Oh
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs. ICML (2020)
Zinan Lin, Alankar Jain, Chen Wang, Giulia C. Fanti, Vyas Sekar
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions. Internet Measurement Conference (2020)
Zinan Lin, Ashish Khetan, Giulia C. Fanti, Sewoong Oh
PacGAN: The Power of Two Samples in Generative Adversarial Networks. IEEE J. Sel. Areas Inf. Theory 1 (1) (2020)
Zinan Lin, Alankar Jain, Chen Wang, Giulia C. Fanti, Vyas Sekar
Generating High-fidelity, Synthetic Time Series Datasets with DoppelGANger. CoRR (2019)
Zinan Lin, Kiran Koshy Thekumparampil, Giulia C. Fanti, Sewoong Oh
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers. CoRR (2019)
Zinan Lin, Soo-Jin Moon, Carolina M. Zarate, Ritika Mulagalapalli, Sekar Kulandaivel, Giulia C. Fanti, Vyas Sekar
Towards Oblivious Network Analysis using Generative Adversarial Networks. HotNets (2019)
Kiran Koshy Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh
Robustness of conditional GANs to noisy labels. NeurIPS (2018)
Kiran Koshy Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh
Robustness of Conditional GANs to Noisy Labels. CoRR (2018)
Zinan Lin, Yongfeng Huang, Jilong Wang
RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network. IEEE Trans. Inf. Forensics Secur. 13 (7) (2018)
Zinan Lin, Ashish Khetan, Giulia C. Fanti, Sewoong Oh
PacGAN: The power of two samples in generative adversarial networks. NeurIPS (2018)
Zinan Lin, Ashish Khetan, Giulia C. Fanti, Sewoong Oh
PacGAN: The power of two samples in generative adversarial networks. CoRR (2017)