Project Laplace

出版物

表示方法:

- DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
  
  Chen Gong, Kecen Li, Zinan Lin, Tianhao Wang
  
  CCS 2025 | July 2025
  
  Publication
- Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models
  
  Zinan Lin, Tadas Baltrusaitis, Sergey Yekhanin
  
  February 2025
  
  Publication
- Inferentially-Private Private Information
  
  Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
  
  WWW 2025 | February 2025
  
  Publication
- MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
  
  Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
  
  ICLR 2025 | February 2025
  
  Publication
- Differentially Private Synthetic Data via Foundation Model APIs 2: Text
  
  Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
  
  ICML 2024 | July 2024
  
  Spotlight
  
  Publication
- Statistic Maximal Leakage
  
  Shuaiqi Wang, Zinan Lin, Giulia Fanti
  
  IEEE International Symposium on Information Theory 2024 | July 2024
  
  Publication
- Differentially Private Synthetic Data via Foundation Model APIs 1: Images
  
  Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
  
  ICLR 2024 | May 2024
  
  Publication Publication Github
- Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
  
  Xinyu Tang, Richard Shin, Huseyin Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Robert Sim
  
  ICLR 2024 | May 2024
  
  Publication Publication Github
- Privately Aligning Language Models with Reinforcement Learning
  
  Fan Wu, Huseyin Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan (Jana) Kulkarni, Robert Sim
  
  ICLR 2024 | May 2024
  
  DOI
- Efficiently Computing Similarities to Private Datasets
  
  Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski
  
  ICLR 2024 | May 2024
  
  Publication
- Selective Pre-training for Private Fine-tuning
  
  Da Yu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
  
  TMLR | May 2024
  
  Publication Publication
- Summary Statistic Privacy in Data Sharing
  
  Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
  
  IEEE Journal on Selected Areas in Information Theory | March 2024
  
  Publication
- Differentially Private Training of Mixture of Experts Models
  
  Pierre Tholoniat, Huseyin A. Inan, Janardhan (Jana) Kulkarni, Robert Sim
  
  PPAI-24: The 5th AAAI Workshop on Privacy-Preserving Artificial Intelligence | February 2024
  
  DOI
- Advancing differential privacy: where we are now and future directions for real-world deployment
  
  Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Da Yu, Sergey Yekhanin, Huanyu Zhang, Wanrong Zhang
  
  Harvard Data Science Review | January 2024, 巻6(1)
  
  Publication
- Differentially Private Approximate Near Neighbor Counting in High Dimensions
  
  Alexandr Andoni, Piotr Indyk, Sepideh Mahabadi, Shyam Narayanan
  
  NeurIPS 2023 | December 2023
  
  Spotlight Presentation
  
  Publication
- Training Private and Efficient Language Models with Synthetic Data from LLMs
  
  Da Yu, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Janardhan (Jana) Kulkarni, Zinan Lin, Chulin Xie, Huishuai Zhang, Wanrong Zhang
  
  NeurIPS 2023 SoLaR Workshop | December 2023
  
  Publication
- Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
  
  Ruixiang Tang, Gord Lueck, Rodolfo Quispe, Huseyin A. Inan, Janardhan (Jana) Kulkarni, Xia Hu
  
  Empirical Methods in Natural Language Processing (EMNLP 2023) | October 2023
  
  DOI PDF
- 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 2023 | June 2023
  
  Outstanding Paper Award
  
  Publication
- Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
  
  Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan (Jana) Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
  
  International Conference on Learning Representations (ICLR) 2023 | May 2023
  
  Publication
- Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
  
  COLT 2023 | February 2023
  
  Publication
- Near-Optimal Correlation Clustering with Privacy
  
  Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski
  
  NeurIPS 2022 | November 2022
  
  Publication PDF
- Differentially Private Model Compression
  
  Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
  
  NeurIPS 2022 | November 2022
  
  Publication
- When does differentially private learning not suffer in high dimensions?
  
  Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin Inan, Janardhan (Jana) Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
  
  NeurIPS’22 | November 2022
  
  Publication
- Private Convex Optimization in General Norms
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
  
  SODA 2023 | July 2022
  
  Publication
- Differentially private fine-tuning of language models
  
  Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan (Jana) Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
  
  ICLR 2022 | April 2022
  
  Publication
- Private Convex Optimization via Exponential Mechanism
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu
  
  COLT 2022 | March 2022
  
  Publication
- Differentially private n-gram extraction
  
  Kunho Kim, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Sergey Yekhanin
  
  NeurIPS 2021 | December 2021
  
  PDF Publication
- Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
  
  Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan (Jana) Kulkarni
  
  2021 International Conference on Machine Learning | July 2021
  
  Publication
- Numerical Composition of Differential Privacy
  
  Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
  
  NeurIPS 2021 | June 2021
  
  Spotlight at NeurIPS 2021.
  
  Preprint Github
- Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
  
  Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
  
  NeurIPS 2021 | March 2021
  
  Publication
- Differentially Private Correlation Clustering
  
  Mark Bun, Marek Eliás, Janardhan (Jana) Kulkarni
  
  2021 International Conference on Machine Learning | February 2021
  
  Publication
- Fast and Memory Efficient Differentially Private-SGD via JL Projections
  
  Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
  
  NeurIPS 2021 | February 2021
  
  Preprint
- Differentially Private Set Union
  
  Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
  
  International Conference on Machine Learning 2020 | July 2020
  
  Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
  
  Publication Github
- Locally Private Hypothesis Selection
  
  Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
  
  Conference on Learning Theory (COLT) 2020 | July 2020
  
  Publication
- Privately Learning Markov Random Fields
  
  Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
  
  ICML 2020 | July 2020
  
  Publication PDF
- Differentially private release of synthetic graphs
  
  Marek Eliáš, Michael Kapralov, Janardhan (Jana) Kulkarni, Yin Tat Lee
  
  2020 Symposium on Discrete Algorithms | January 2020
  
  PDF
- Locally Private Gaussian Estimation
  
  Matthew Joseph, Janardhan (Jana) Kulkarni, Jieming Mao, Zhiwei Steven Wu
  
  November 2019
  
  Publication PDF
- An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
  
  Joshua Allen, Bolin Ding, Janardhan (Jana) Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
  
  NeurIPS 2019 | August 2019
  
  Publication Publication プロジェクト
- Collecting Telemetry Data Privately
  
  Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
  
  Advances in Neural Information Processing Systems 30 | December 2017
  
  Publication

- DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
  
  Chen Gong, Kecen Li, Zinan Lin, Tianhao Wang
  
  CCS 2025 | July 2025
  
  Publication
- Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models
  
  Zinan Lin, Tadas Baltrusaitis, Sergey Yekhanin
  
  February 2025
  
  Publication
- Inferentially-Private Private Information
  
  Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
  
  WWW 2025 | February 2025
  
  Publication
- MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
  
  Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
  
  ICLR 2025 | February 2025
  
  Publication
- Differentially Private Synthetic Data via Foundation Model APIs 2: Text
  
  Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
  
  ICML 2024 | July 2024
  
  Spotlight
  
  Publication
- Statistic Maximal Leakage
  
  Shuaiqi Wang, Zinan Lin, Giulia Fanti
  
  IEEE International Symposium on Information Theory 2024 | July 2024
  
  Publication
- Privately Aligning Language Models with Reinforcement Learning
  
  Fan Wu, Huseyin Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan (Jana) Kulkarni, Robert Sim
  
  ICLR 2024 | May 2024
  
  DOI
- Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
  
  Xinyu Tang, Richard Shin, Huseyin Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Robert Sim
  
  ICLR 2024 | May 2024
  
  Publication Publication Github
- Differentially Private Synthetic Data via Foundation Model APIs 1: Images
  
  Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
  
  ICLR 2024 | May 2024
  
  Publication Publication Github
- Summary Statistic Privacy in Data Sharing
  
  Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
  
  IEEE Journal on Selected Areas in Information Theory | March 2024
  
  Publication
- Training Private and Efficient Language Models with Synthetic Data from LLMs
  
  Da Yu, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Janardhan (Jana) Kulkarni, Zinan Lin, Chulin Xie, Huishuai Zhang, Wanrong Zhang
  
  NeurIPS 2023 SoLaR Workshop | December 2023
  
  Publication
- Differentially Private Approximate Near Neighbor Counting in High Dimensions
  
  Alexandr Andoni, Piotr Indyk, Sepideh Mahabadi, Shyam Narayanan
  
  NeurIPS 2023 | December 2023
  
  Spotlight Presentation
  
  Publication
- 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 2023 | June 2023
  
  Outstanding Paper Award
  
  Publication
- Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
  
  Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan (Jana) Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
  
  International Conference on Learning Representations (ICLR) 2023 | May 2023
  
  Publication
- Near-Optimal Correlation Clustering with Privacy
  
  Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski
  
  NeurIPS 2022 | November 2022
  
  Publication PDF
- Differentially Private Model Compression
  
  Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
  
  NeurIPS 2022 | November 2022
  
  Publication
- Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
  
  Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan (Jana) Kulkarni
  
  2021 International Conference on Machine Learning | July 2021
  
  Publication
- Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
  
  Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
  
  NeurIPS 2021 | March 2021
  
  Publication
- Privately Learning Markov Random Fields
  
  Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
  
  ICML 2020 | July 2020
  
  Publication PDF
- DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
  
  Chen Gong, Kecen Li, Zinan Lin, Tianhao Wang
  
  CCS 2025 | July 2025
  
  Publication
- Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models
  
  Zinan Lin, Tadas Baltrusaitis, Sergey Yekhanin
  
  February 2025
  
  Publication
- Inferentially-Private Private Information
  
  Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
  
  WWW 2025 | February 2025
  
  Publication
- MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
  
  Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
  
  ICLR 2025 | February 2025
  
  Publication
- Differentially Private Synthetic Data via Foundation Model APIs 2: Text
  
  Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
  
  ICML 2024 | July 2024
  
  Spotlight
  
  Publication
- Statistic Maximal Leakage
  
  Shuaiqi Wang, Zinan Lin, Giulia Fanti
  
  IEEE International Symposium on Information Theory 2024 | July 2024
  
  Publication
- Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
  
  Xinyu Tang, Richard Shin, Huseyin Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Robert Sim
  
  ICLR 2024 | May 2024
  
  Publication Publication Github
- Differentially Private Synthetic Data via Foundation Model APIs 1: Images
  
  Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
  
  ICLR 2024 | May 2024
  
  Publication Publication Github
- Efficiently Computing Similarities to Private Datasets
  
  Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski
  
  ICLR 2024 | May 2024
  
  Publication
- Selective Pre-training for Private Fine-tuning
  
  Da Yu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
  
  TMLR | May 2024
  
  Publication Publication
- Summary Statistic Privacy in Data Sharing
  
  Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
  
  IEEE Journal on Selected Areas in Information Theory | March 2024
  
  Publication
- Advancing differential privacy: where we are now and future directions for real-world deployment
  
  Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Da Yu, Sergey Yekhanin, Huanyu Zhang, Wanrong Zhang
  
  Harvard Data Science Review | January 2024, 巻6(1)
  
  Publication
- Differentially Private Approximate Near Neighbor Counting in High Dimensions
  
  Alexandr Andoni, Piotr Indyk, Sepideh Mahabadi, Shyam Narayanan
  
  NeurIPS 2023 | December 2023
  
  Spotlight Presentation
  
  Publication
- 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 2023 | June 2023
  
  Outstanding Paper Award
  
  Publication
- Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
  
  Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan (Jana) Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
  
  International Conference on Learning Representations (ICLR) 2023 | May 2023
  
  Publication
- Private Convex Optimization in General Norms
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
  
  SODA 2023 | July 2022
  
  Publication
- Private Convex Optimization via Exponential Mechanism
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu
  
  COLT 2022 | March 2022
  
  Publication
- Numerical Composition of Differential Privacy
  
  Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
  
  NeurIPS 2021 | June 2021
  
  Spotlight at NeurIPS 2021.
  
  Preprint Github
- Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
  
  Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
  
  NeurIPS 2021 | March 2021
  
  Publication
- Fast and Memory Efficient Differentially Private-SGD via JL Projections
  
  Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
  
  NeurIPS 2021 | February 2021
  
  Preprint
- Privately Learning Markov Random Fields
  
  Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
  
  ICML 2020 | July 2020
  
  Publication PDF
- Locally Private Hypothesis Selection
  
  Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
  
  Conference on Learning Theory (COLT) 2020 | July 2020
  
  Publication
- Differentially Private Set Union
  
  Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
  
  International Conference on Machine Learning 2020 | July 2020
  
  Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
  
  Publication Github
- Locally Private Gaussian Estimation
  
  Matthew Joseph, Janardhan (Jana) Kulkarni, Jieming Mao, Zhiwei Steven Wu
  
  November 2019
  
  Publication PDF
- An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
  
  Joshua Allen, Bolin Ding, Janardhan (Jana) Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
  
  NeurIPS 2019 | August 2019
  
  Publication Publication プロジェクト
- Collecting Telemetry Data Privately
  
  Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
  
  Advances in Neural Information Processing Systems 30 | December 2017
  
  Publication
- MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
  
  Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
  
  ICLR 2025 | February 2025
  
  Publication
- Differentially Private Synthetic Data via Foundation Model APIs 2: Text
  
  Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
  
  ICML 2024 | July 2024
  
  Spotlight
  
  Publication
- Privately Aligning Language Models with Reinforcement Learning
  
  Fan Wu, Huseyin Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan (Jana) Kulkarni, Robert Sim
  
  ICLR 2024 | May 2024
  
  DOI
- Differentially Private Synthetic Data via Foundation Model APIs 1: Images
  
  Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
  
  ICLR 2024 | May 2024
  
  Publication Publication Github
- Efficiently Computing Similarities to Private Datasets
  
  Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski
  
  ICLR 2024 | May 2024
  
  Publication
- Selective Pre-training for Private Fine-tuning
  
  Da Yu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
  
  TMLR | May 2024
  
  Publication Publication
- Differentially Private Training of Mixture of Experts Models
  
  Pierre Tholoniat, Huseyin A. Inan, Janardhan (Jana) Kulkarni, Robert Sim
  
  PPAI-24: The 5th AAAI Workshop on Privacy-Preserving Artificial Intelligence | February 2024
  
  DOI
- Differentially Private Approximate Near Neighbor Counting in High Dimensions
  
  Alexandr Andoni, Piotr Indyk, Sepideh Mahabadi, Shyam Narayanan
  
  NeurIPS 2023 | December 2023
  
  Spotlight Presentation
  
  Publication
- Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
  
  Ruixiang Tang, Gord Lueck, Rodolfo Quispe, Huseyin A. Inan, Janardhan (Jana) Kulkarni, Xia Hu
  
  Empirical Methods in Natural Language Processing (EMNLP 2023) | October 2023
  
  DOI PDF
- Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
  
  COLT 2023 | February 2023
  
  Publication
- Near-Optimal Correlation Clustering with Privacy
  
  Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski
  
  NeurIPS 2022 | November 2022
  
  Publication PDF
- Differentially Private Model Compression
  
  Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
  
  NeurIPS 2022 | November 2022
  
  Publication
- When does differentially private learning not suffer in high dimensions?
  
  Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin Inan, Janardhan (Jana) Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
  
  NeurIPS’22 | November 2022
  
  Publication
- Private Convex Optimization in General Norms
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
  
  SODA 2023 | July 2022
  
  Publication
- Differentially private fine-tuning of language models
  
  Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan (Jana) Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
  
  ICLR 2022 | April 2022
  
  Publication
- Private Convex Optimization via Exponential Mechanism
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu
  
  COLT 2022 | March 2022
  
  Publication
- Differentially private n-gram extraction
  
  Kunho Kim, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Sergey Yekhanin
  
  NeurIPS 2021 | December 2021
  
  PDF Publication
- Numerical Composition of Differential Privacy
  
  Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
  
  NeurIPS 2021 | June 2021
  
  Spotlight at NeurIPS 2021.
  
  Preprint Github
- Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
  
  Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
  
  NeurIPS 2021 | March 2021
  
  Publication
- Differentially Private Correlation Clustering
  
  Mark Bun, Marek Eliás, Janardhan (Jana) Kulkarni
  
  2021 International Conference on Machine Learning | February 2021
  
  Publication
- Fast and Memory Efficient Differentially Private-SGD via JL Projections
  
  Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
  
  NeurIPS 2021 | February 2021
  
  Preprint
- Privately Learning Markov Random Fields
  
  Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
  
  ICML 2020 | July 2020
  
  Publication PDF
- Locally Private Hypothesis Selection
  
  Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
  
  Conference on Learning Theory (COLT) 2020 | July 2020
  
  Publication
- Differentially Private Set Union
  
  Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
  
  International Conference on Machine Learning 2020 | July 2020
  
  Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
  
  Publication Github
- Differentially private release of synthetic graphs
  
  Marek Eliáš, Michael Kapralov, Janardhan (Jana) Kulkarni, Yin Tat Lee
  
  2020 Symposium on Discrete Algorithms | January 2020
  
  PDF
- Collecting Telemetry Data Privately
  
  Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
  
  Advances in Neural Information Processing Systems 30 | December 2017
  
  Publication
- Differentially Private Synthetic Data via Foundation Model APIs 2: Text
  
  Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
  
  ICML 2024 | July 2024
  
  Spotlight
  
  Publication
- Differentially Private Synthetic Data via Foundation Model APIs 1: Images
  
  Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
  
  ICLR 2024 | May 2024
  
  Publication Publication Github
- Advancing differential privacy: where we are now and future directions for real-world deployment
  
  Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Da Yu, Sergey Yekhanin, Huanyu Zhang, Wanrong Zhang
  
  Harvard Data Science Review | January 2024, 巻6(1)
  
  Publication
- Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
  
  COLT 2023 | February 2023
  
  Publication
- Private Convex Optimization in General Norms
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
  
  SODA 2023 | July 2022
  
  Publication
- Private Convex Optimization via Exponential Mechanism
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu
  
  COLT 2022 | March 2022
  
  Publication
- Numerical Composition of Differential Privacy
  
  Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
  
  NeurIPS 2021 | June 2021
  
  Spotlight at NeurIPS 2021.
  
  Preprint Github
- Fast and Memory Efficient Differentially Private-SGD via JL Projections
  
  Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
  
  NeurIPS 2021 | February 2021
  
  Preprint
- Locally Private Hypothesis Selection
  
  Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
  
  Conference on Learning Theory (COLT) 2020 | July 2020
  
  Publication
- Differentially Private Set Union
  
  Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
  
  International Conference on Machine Learning 2020 | July 2020
  
  Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
  
  Publication Github
- Collecting Telemetry Data Privately
  
  Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
  
  Advances in Neural Information Processing Systems 30 | December 2017
  
  Publication

- DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis
  
  Chen Gong, Kecen Li, Zinan Lin, Tianhao Wang
  
  CCS 2025 | July 2025
  
  Publication
- Inferentially-Private Private Information
  
  Shuaiqi Wang, Shuran Zheng, Zinan Lin, Giulia Fanti, Zhiwei Steven Wu
  
  WWW 2025 | February 2025
  
  Publication
- MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
  
  Chejian Xu, Jiawei Zhang, Zhaorun Chen, Chulin Xie, Mintong Kang, Zhuowen Yuan, Zidi Xiong, Chenhui Zhang, Lingzhi Yuan, Yi Zeng, Peiyang Xu, Chengquan Guo, Andy Zhou, Jeffrey Ziwei Tan, Zhun Wang, Alexander Xiong, Xuandong Zhao, Yu Gai, Francesco Pinto, Yujin Potter, Zhen Xiang, Zinan Lin, Dan Hendrycks, Dawn Song, Bo Li
  
  ICLR 2025 | February 2025
  
  Publication
- Differentially Private Synthetic Data via Foundation Model APIs 2: Text
  
  Chulin Xie, Zinan Lin, Arturs Backurs, Sivakanth Gopi, Da Yu, Huseyin Inan, Harsha Nori, Haotian Jiang, Huishuai Zhang, Yin Tat Lee, Bo Li, Sergey Yekhanin
  
  ICML 2024 | July 2024
  
  Spotlight
  
  Publication
- Statistic Maximal Leakage
  
  Shuaiqi Wang, Zinan Lin, Giulia Fanti
  
  IEEE International Symposium on Information Theory 2024 | July 2024
  
  Publication
- Privately Aligning Language Models with Reinforcement Learning
  
  Fan Wu, Huseyin Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan (Jana) Kulkarni, Robert Sim
  
  ICLR 2024 | May 2024
  
  DOI
- Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
  
  Xinyu Tang, Richard Shin, Huseyin Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Robert Sim
  
  ICLR 2024 | May 2024
  
  Publication Publication Github
- Differentially Private Synthetic Data via Foundation Model APIs 1: Images
  
  Zinan Lin, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Harsha Nori, Sergey Yekhanin
  
  ICLR 2024 | May 2024
  
  Publication Publication Github
- Efficiently Computing Similarities to Private Datasets
  
  Arturs Backurs, Zinan Lin, Sepideh Mahabadi, Sandeep Silwal, Jakub Tarnawski
  
  ICLR 2024 | May 2024
  
  Publication
- Differentially Private Training of Mixture of Experts Models
  
  Pierre Tholoniat, Huseyin A. Inan, Janardhan (Jana) Kulkarni, Robert Sim
  
  PPAI-24: The 5th AAAI Workshop on Privacy-Preserving Artificial Intelligence | February 2024
  
  DOI
- Training Private and Efficient Language Models with Synthetic Data from LLMs
  
  Da Yu, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Janardhan (Jana) Kulkarni, Zinan Lin, Chulin Xie, Huishuai Zhang, Wanrong Zhang
  
  NeurIPS 2023 SoLaR Workshop | December 2023
  
  Publication
- Differentially Private Approximate Near Neighbor Counting in High Dimensions
  
  Alexandr Andoni, Piotr Indyk, Sepideh Mahabadi, Shyam Narayanan
  
  NeurIPS 2023 | December 2023
  
  Spotlight Presentation
  
  Publication
- Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
  
  Ruixiang Tang, Gord Lueck, Rodolfo Quispe, Huseyin A. Inan, Janardhan (Jana) Kulkarni, Xia Hu
  
  Empirical Methods in Natural Language Processing (EMNLP 2023) | October 2023
  
  DOI PDF
- 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 2023 | June 2023
  
  Outstanding Paper Award
  
  Publication
- Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping
  
  Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan (Jana) Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian
  
  International Conference on Learning Representations (ICLR) 2023 | May 2023
  
  Publication
- Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
  
  COLT 2023 | February 2023
  
  Publication
- Near-Optimal Correlation Clustering with Privacy
  
  Vincent Cohen-Addad, Chenglin Fan, Silvio Lattanzi, Slobodan Mitrović, Ashkan Norouzi-Fard, Nikos Parotsidis, Jakub Tarnawski
  
  NeurIPS 2022 | November 2022
  
  Publication PDF
- Differentially Private Model Compression
  
  Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
  
  NeurIPS 2022 | November 2022
  
  Publication
- When does differentially private learning not suffer in high dimensions?
  
  Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin Inan, Janardhan (Jana) Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
  
  NeurIPS’22 | November 2022
  
  Publication
- Private Convex Optimization in General Norms
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Rouqi Shen, Kevin Tian
  
  SODA 2023 | July 2022
  
  Publication
- Differentially private fine-tuning of language models
  
  Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Gautam Kamath, Janardhan (Jana) Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang
  
  ICLR 2022 | April 2022
  
  Publication
- Private Convex Optimization via Exponential Mechanism
  
  Sivakanth Gopi, Yin Tat Lee, Daogao Liu
  
  COLT 2022 | March 2022
  
  Publication
- Differentially private n-gram extraction
  
  Kunho Kim, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Sergey Yekhanin
  
  NeurIPS 2021 | December 2021
  
  PDF Publication
- Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
  
  Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan (Jana) Kulkarni
  
  2021 International Conference on Machine Learning | July 2021
  
  Publication
- Numerical Composition of Differential Privacy
  
  Sivakanth Gopi, Yin Tat Lee, Lukas Wutschitz
  
  NeurIPS 2021 | June 2021
  
  Spotlight at NeurIPS 2021.
  
  Preprint Github
- Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps
  
  Janardhan (Jana) Kulkarni, Yin Tat Lee, Daogao Liu
  
  NeurIPS 2021 | March 2021
  
  Publication
- Differentially Private Correlation Clustering
  
  Mark Bun, Marek Eliás, Janardhan (Jana) Kulkarni
  
  2021 International Conference on Machine Learning | February 2021
  
  Publication
- Fast and Memory Efficient Differentially Private-SGD via JL Projections
  
  Zhiqi Bu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Yin Tat Lee, Judy Hanwen Shen, Uthaipon Tantipongpipat
  
  NeurIPS 2021 | February 2021
  
  Preprint
- Privately Learning Markov Random Fields
  
  Huanyu Zhang, Gautam Kamath, Janardhan (Jana) Kulkarni, Zhiwei Steven Wu
  
  ICML 2020 | July 2020
  
  Publication PDF
- Locally Private Hypothesis Selection
  
  Sivakanth Gopi, Gautam Kamath, Janardhan (Jana) Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang
  
  Conference on Learning Theory (COLT) 2020 | July 2020
  
  Publication
- Differentially Private Set Union
  
  Sivakanth Gopi, Pankaj Gulhane, Janardhan (Jana) Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin
  
  International Conference on Machine Learning 2020 | July 2020
  
  Contributed talk at TPDP 2020. Also appeared in the Journal of Privacy and Confidentiality (JPC).
  
  Publication Github
- Differentially private release of synthetic graphs
  
  Marek Eliáš, Michael Kapralov, Janardhan (Jana) Kulkarni, Yin Tat Lee
  
  2020 Symposium on Discrete Algorithms | January 2020
  
  PDF
- An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors
  
  Joshua Allen, Bolin Ding, Janardhan (Jana) Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
  
  NeurIPS 2019 | August 2019
  
  Publication Publication プロジェクト
- Collecting Telemetry Data Privately
  
  Bolin Ding, Janardhan (Jana) Kulkarni, Sergey Yekhanin
  
  Advances in Neural Information Processing Systems 30 | December 2017
  
  Publication
- Differentially Private Synthetic Data via APIs 3: Using Simulators Instead of Foundation Models
  
  Zinan Lin, Tadas Baltrusaitis, Sergey Yekhanin
  
  February 2025
  
  Publication
- Selective Pre-training for Private Fine-tuning
  
  Da Yu, Sivakanth Gopi, Janardhan (Jana) Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang
  
  TMLR | May 2024
  
  Publication Publication
- Summary Statistic Privacy in Data Sharing
  
  Zinan Lin, Shuaiqi Wang, Vyas Sekar, Giulia Fanti
  
  IEEE Journal on Selected Areas in Information Theory | March 2024
  
  Publication
- Advancing differential privacy: where we are now and future directions for real-world deployment
  
  Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu, Yangsibo Huang, Matthew Jagielski, Peter Kairouz, Gautam Kamath, Sewoong Oh, Olga Ohrimenko, Nicolas Papernot, Ryan Rogers, Milan Shen, Shuang Song, Weijie Su, Andreas Terzis, Abhradeep Thakurta, Sergei Vassilvitskii, Yu-Xiang Wang, Li Xiong, Da Yu, Sergey Yekhanin, Huanyu Zhang, Wanrong Zhang
  
  Harvard Data Science Review | January 2024, 巻6(1)
  
  Publication
- Locally Private Gaussian Estimation
  
  Matthew Joseph, Janardhan (Jana) Kulkarni, Jieming Mao, Zhiwei Steven Wu
  
  November 2019
  
  Publication PDF