Zinan Lin
Zinan Lin
Microsoft Research (Redmond), Carnegie Mellon University
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Citado por
Citado por
Pacgan: The power of two samples in generative adversarial networks
Z Lin, A Khetan, G Fanti, S Oh
NeurIPS 2018 & IEEE JSAIT 2020, 2018
Robustness of conditional GANs to noisy labels
KK Thekumparampil, A Khetan, Z Lin, S Oh
NeurIPS 2018, 2018
Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
Z Lin, A Jain, C Wang, G Fanti, V Sekar
ACM IMC 2020, 2020
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
B Wang*, W Chen*, H Pei*, C Xie*, M Kang*, C Zhang*, C Xu, Z Xiong, ...
NeurIPS 2023, 2023
InfoGAN-CR and ModelCentrality: Self-supervised Model Training and Selection for Disentangling GANs
Z Lin, KK Thekumparampil, G Fanti, S Oh
ICML 2020, 2020
RNN-SM: Fast Steganalysis of VoIP Streams Using Recurrent Neural Network
Z Lin, Y Huang, J Wang
IEEE TIFS 2018, 2018
MLGO: a Machine Learning Guided Compiler Optimizations Framework
M Trofin*, Y Qian*, E Brevdo, Z Lin, K Choromanski, D Li
arXiv preprint arXiv:2101.04808, 2021
Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding
X Ning*, Z Lin*, Z Zhou*, H Yang, Y Wang
ICLR 2024, 2024
Practical GAN-based synthetic IP header trace generation using NetShare
Y Yin, Z Lin, M Jin, G Fanti, V Sekar
SIGCOMM 2022, 2022
Why Spectral Normalization Stabilizes GANs: Analysis and Improvements
Z Lin, V Sekar, G Fanti
NeurIPS 2021, 2021
On the Privacy Properties of GAN-generated Samples
Z Lin, V Sekar, G Fanti
AISTATS 2021, 2021
Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions
T Huster, JEJ Cohen, Z Lin, K Chan, C Kamhoua, N Leslie, CYJ Chiang, ...
ICML 2021, 2021
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
X Tang, R Shin, HA Inan, A Manoel, F Mireshghallah, Z Lin, S Gopi, ...
ICLR 2024, 2024
OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models
E Liu*, X Ning*, Z Lin*, H Yang, Y Wang
ICML 2023, 2023
Selective Pre-training for Private Fine-tuning
D Yu, S Gopi, J Kulkarni, Z Lin, S Naik, TL Religa, J Yin, H Zhang
TMLR, 2024
Differentially Private Synthetic Data via Foundation Model APIs 1: Images
Z Lin, S Gopi, J Kulkarni, H Nori, S Yekhanin
ICLR 2024, 2024
RareGAN: Generating Samples for Rare Classes
Z Lin, H Liang, G Fanti, V Sekar
AAAI 2022, 2022
Towards Oblivious Network Analysis using Generative Adversarial Networks
Z Lin, SJ Moon, CM Zarate, R Mulagalapalli, S Kulandaivel, G Fanti, ...
HotNets 2019, 2019
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
C Xie, Z Lin, A Backurs, S Gopi, D Yu, HA Inan, H Nori, H Jiang, H Zhang, ...
arXiv preprint arXiv:2403.01749, 2024
Summary Statistic Privacy in Data Sharing
Z Lin*, S Wang*, V Sekar, G Fanti
arXiv preprint arXiv:2303.02014, 2023
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Artículos 1–20