On the Emergence of Thinking in LLMs I: Searching for the Right Intuition
Guanghao Ye, Khiem Duc Pham, Xinzhi Zhang, Sivakanth Gopi, Baolin Peng, Beibin Li, Janardhan (Jana) Kulkarni, Huseyin Inan
February 2025
Papiers de Collaboration
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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
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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
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Privately Aligning Language Models with Reinforcement Learning
Fan Wu, Huseyin Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan (Jana) Kulkarni, Robert Sim
ICLR 2024 | May 2024
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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
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Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Huan Sun, David Levitan, Robert Sim
ACL 2023 | July 2023
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Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
NeurIPS 2022 | November 2022
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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
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dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, Lukas Wutschitz
July 2022
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Active Data Pattern Extraction Attacks on Generative Language Models
Esha Ghosh, Bargav Jayaraman, Sambuddha Roy, Wei Dai, Huseyin Inan, Melissa Chase
MSR-TR-2022-23 | July 2022
Publié par Microsoft
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Privacy Leakage in Text Classification: A Data Extraction Approach
Adel Elmahdy, Huseyin Inan, Robert Sim
June 2022
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Membership Inference Attacks Against NLP Classification Models
Virat Shejwalkar, Huseyin Inan, Amir Houmansadr, Robert Sim
NeurIPS 2021 Workshop Privacy in Machine Learning | September 2021
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Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
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On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
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Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
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On the Emergence of Thinking in LLMs I: Searching for the Right Intuition
Guanghao Ye, Khiem Duc Pham, Xinzhi Zhang, Sivakanth Gopi, Baolin Peng, Beibin Li, Janardhan (Jana) Kulkarni, Huseyin Inan
February 2025
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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
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Privately Aligning Language Models with Reinforcement Learning
Fan Wu, Huseyin Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan (Jana) Kulkarni, Robert Sim
ICLR 2024 | May 2024
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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
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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
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Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Huan Sun, David Levitan, Robert Sim
ACL 2023 | July 2023
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Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
NeurIPS 2022 | November 2022
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dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, Lukas Wutschitz
July 2022
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Active Data Pattern Extraction Attacks on Generative Language Models
Esha Ghosh, Bargav Jayaraman, Sambuddha Roy, Wei Dai, Huseyin Inan, Melissa Chase
MSR-TR-2022-23 | July 2022
Publié par Microsoft
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Membership Inference Attacks Against NLP Classification Models
Virat Shejwalkar, Huseyin Inan, Amir Houmansadr, Robert Sim
NeurIPS 2021 Workshop Privacy in Machine Learning | September 2021
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Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
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On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
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Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
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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
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Privately Aligning Language Models with Reinforcement Learning
Fan Wu, Huseyin Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan (Jana) Kulkarni, Robert Sim
ICLR 2024 | May 2024
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Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
NeurIPS 2022 | November 2022
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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
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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
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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
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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
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Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Huan Sun, David Levitan, Robert Sim
ACL 2023 | July 2023
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dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, Lukas Wutschitz
July 2022
-
Active Data Pattern Extraction Attacks on Generative Language Models
Esha Ghosh, Bargav Jayaraman, Sambuddha Roy, Wei Dai, Huseyin Inan, Melissa Chase
MSR-TR-2022-23 | July 2022
Publié par Microsoft
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Privacy Leakage in Text Classification: A Data Extraction Approach
Adel Elmahdy, Huseyin Inan, Robert Sim
June 2022
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Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
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On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
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Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
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Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
-
On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
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Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
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On the Emergence of Thinking in LLMs I: Searching for the Right Intuition
Guanghao Ye, Khiem Duc Pham, Xinzhi Zhang, Sivakanth Gopi, Baolin Peng, Beibin Li, Janardhan (Jana) Kulkarni, Huseyin Inan
February 2025
-
dp-transformers: Training transformer models with differential privacy
Huseyin Inan, Andre Manoel, Lukas Wutschitz
July 2022
-
Membership Inference on Word Embedding and Beyond
Saeed Mahloujifar, Huseyin A. Inan, Melissa Chase, Esha Ghosh, Marcello Hasegawa
June 2021
-
Training Data Leakage Analysis in Language Models
Huseyin A. Inan, Osman Ramadan, Lukas Wutschitz, Daniel Jones, Victor Rühle, James Withers, Robert Sim
February 2021
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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
-
Privately Aligning Language Models with Reinforcement Learning
Fan Wu, Huseyin Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan (Jana) Kulkarni, Robert Sim
ICLR 2024 | May 2024
-
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
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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
-
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe
Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Huan Sun, David Levitan, Robert Sim
ACL 2023 | July 2023
-
Differentially Private Model Compression
Fatemehsadat Mireshghallah, Arturs Backurs, Huseyin Inan, Lukas Wutschitz, Janardhan (Jana) Kulkarni
NeurIPS 2022 | November 2022
-
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
-
Membership Inference Attacks Against NLP Classification Models
Virat Shejwalkar, Huseyin Inan, Amir Houmansadr, Robert Sim
NeurIPS 2021 Workshop Privacy in Machine Learning | September 2021
-
-
-
Active Data Pattern Extraction Attacks on Generative Language Models
Esha Ghosh, Bargav Jayaraman, Sambuddha Roy, Wei Dai, Huseyin Inan, Melissa Chase
MSR-TR-2022-23 | July 2022
Publié par Microsoft
-
-
-
Privacy Leakage in Text Classification: A Data Extraction Approach
Adel Elmahdy, Huseyin Inan, Robert Sim
June 2022
-
On Privacy and Confidentiality of Communications in Organizational Graphs
Masoumeh Shafieinejad, Huseyin A. Inan (huinan), Marcello Hasegawa, Robert Sim
May 2021
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