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Zinan Lin 0001
Person information
- affiliation: Microsoft Research, Redmond, WA, USA
- affiliation (former): Carnegie Mellon University, Pittsburgh, PA, USA
Other persons with the same name
- Zinan Lin 0002 — InterDigital Communications, Inc., USA
- Zinan Lin 0003 — Polytechnical University Brooklyn, NY, USA
- Zinan Lin 0004
— Shenzhen University, China
- Zinan Lin 0005 — Hangzhou Dianzi University, School of Science, China
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2020 – today
- 2024
- [j4]Zinan Lin, Shuaiqi Wang
Summary Statistic Privacy in Data Sharing. IEEE J. Sel. Areas Inf. Theory 5: 369-384 (2024) - [j3]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) - [c19]Ronghao Ni, Zinan Lin, Shuaiqi Wang, Giulia Fanti:
Mixture-of-Linear-Experts for Long-term Time Series Forecasting. AISTATS 2024: 4672-4680 - [c18]Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin:
Differentially Private Synthetic Data via Foundation Model APIs 1: Images. ICLR 2024 - [c17]Junyi Zhu, Zinan Lin, Enshu Liu, Xuefei Ning, Matthew B. Blaschko:
Rescaling Intermediate Features Makes Trained Consistency Models Perform Better. Tiny Papers @ ICLR 2024 - [c16]Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski:
Efficiently Computing Similarities to Private Datasets. ICLR 2024 - [c15]Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, Huazhong Yang, Yu Wang:
Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generation. ICLR 2024 - [c14]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 - [c13]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. ICML 2024 - [i26]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 abs/2403.01749 (2024) - [i25]Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski:
Efficiently Computing Similarities to Private Datasets. CoRR abs/2403.08917 (2024) - [i24]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 abs/2403.16379 (2024) - [i23]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 abs/2404.02241 (2024) - [i22]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 abs/2405.17873 (2024) - [i21]Sangyun Lee, Zinan Lin, Giulia Fanti:
Improving the Training of Rectified Flows. CoRR abs/2405.20320 (2024) - [i20]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 abs/2406.02540 (2024) - [i19]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 abs/2406.14629 (2024) - [i18]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 abs/2407.00945 (2024) - 2023
- [c12]Enshu Liu, Xuefei Ning, Zinan Lin, Huazhong Yang, Yu Wang:
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models. ICML 2023: 21915-21936 - [c11]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 - [i17]Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti:
Summary Statistic Privacy in Data Sharing. CoRR abs/2303.02014 (2023) - [i16]Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang:
Selective Pre-training for Private Fine-tuning. CoRR abs/2305.13865 (2023) - [i15]Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin:
Differentially Private Synthetic Data via Foundation Model APIs 1: Images. CoRR abs/2305.15560 (2023) - [i14]Enshu Liu, Xuefei Ning, Zinan Lin, Huazhong Yang, Yu Wang:
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models. CoRR abs/2306.08860 (2023) - [i13]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. CoRR abs/2306.11698 (2023) - [i12]Xuefei Ning, Zinan Lin, Zixuan Zhou, Huazhong Yang, Yu Wang:
Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding. CoRR abs/2307.15337 (2023) - [i11]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 abs/2309.11765 (2023) - [i10]Ronghao Ni, Zinan Lin, Shuaiqi Wang, Giulia Fanti:
Mixture-of-Linear-Experts for Long-term Time Series Forecasting. CoRR abs/2312.06786 (2023) - 2022
- [c10]Zinan Lin, Hao Liang, Giulia Fanti, Vyas Sekar:
RareGAN: Generating Samples for Rare Classes. AAAI 2022: 7506-7515 - [c9]Yucheng Yin, Zinan Lin, Minhao Jin, Giulia Fanti, Vyas Sekar:
Practical GAN-based synthetic IP header trace generation using NetShare. SIGCOMM 2022: 458-472 - [i9]Zinan Lin, Hao Liang, Giulia Fanti, Vyas Sekar:
RareGAN: Generating Samples for Rare Classes. CoRR abs/2203.10674 (2022) - [i8]Zinan Lin, Vyas Sekar, Giulia Fanti:
On the Privacy Properties of GAN-generated Samples. CoRR abs/2206.01349 (2022) - 2021
- [c8]Zinan Lin, Vyas Sekar, Giulia Fanti:
On the Privacy Properties of GAN-generated Samples. AISTATS 2021: 1522-1530 - [c7]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: 4523-4532 - [c6]Zinan Lin, Vyas Sekar, Giulia Fanti:
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements. NeurIPS 2021: 9625-9638 - [i7]Mircea Trofin, Yundi Qian, Eugene Brevdo, Zinan Lin, Krzysztof Choromanski, David Li:
MLGO: a Machine Learning Guided Compiler Optimizations Framework. CoRR abs/2101.04808 (2021) - [i6]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 abs/2101.09113 (2021) - 2020
- [j2]Zinan Lin, Ashish Khetan
PacGAN: The Power of Two Samples in Generative Adversarial Networks. IEEE J. Sel. Areas Inf. Theory 1(1): 324-335 (2020) - [c5]Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh:
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs. ICML 2020: 6127-6139 - [c4]Zinan Lin, Alankar Jain, Chen Wang
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions. Internet Measurement Conference 2020: 464-483 - [i5]Zinan Lin, Vyas Sekar, Giulia Fanti:
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements. CoRR abs/2009.02773 (2020)
2010 – 2019
- 2019
- [c3]Zinan Lin, Soo-Jin Moon, Carolina M. Zarate, Ritika Mulagalapalli, Sekar Kulandaivel, Giulia Fanti, Vyas Sekar:
Towards Oblivious Network Analysis using Generative Adversarial Networks. HotNets 2019: 43-51 - [i4]Zinan Lin, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh:
InfoGAN-CR: Disentangling Generative Adversarial Networks with Contrastive Regularizers. CoRR abs/1906.06034 (2019) - [i3]Zinan Lin, Alankar Jain, Chen Wang
Generating High-fidelity, Synthetic Time Series Datasets with DoppelGANger. CoRR abs/1909.13403 (2019) - 2018
- [j1]Zinan Lin
RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network. IEEE Trans. Inf. Forensics Secur. 13(7): 1854-1868 (2018) - [c2]Zinan Lin, Ashish Khetan, Giulia Fanti, Sewoong Oh:
PacGAN: The power of two samples in generative adversarial networks. NeurIPS 2018: 1505-1514 - [c1]Kiran Koshy Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh:
Robustness of conditional GANs to noisy labels. NeurIPS 2018: 10292-10303 - [i2]Kiran Koshy Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh:
Robustness of Conditional GANs to Noisy Labels. CoRR abs/1811.03205 (2018) - 2017
- [i1]Zinan Lin, Ashish Khetan, Giulia Fanti, Sewoong Oh:
PacGAN: The power of two samples in generative adversarial networks. CoRR abs/1712.04086 (2017)
Coauthor Index
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last updated on 2024-09-10 01:13 CEST by the dblp team
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