Kulkarni, Janardhan

28 publications

NeurIPS 2025 Contextual Integrity in LLMs via Reasoning and Reinforcement Learning Guangchen Lan, Huseyin A Inan, Sahar Abdelnabi, Janardhan Kulkarni, Lukas Wutschitz, Reza Shokri, Christopher Brinton, Robert Sim

COLT 2025 DiscQuant: A Quantization Method for Neural Networks Inspired by Discrepancy Theory Jerry Chee, Arturs Backurs, Rainie Heck, Li Zhang, Janardhan Kulkarni, Thomas Rothvoss, Sivakanth Gopi

NeurIPS 2025 SAS: Simulated Attention Score Chuanyang Zheng, Jiankai Sun, Yihang Gao, Yuehao Wang, Peihao Wang, Jing Xiong, Liliang Ren, Hao Cheng, Janardhan Kulkarni, Yelong Shen, Zhangyang Wang, Mac Schwager, Anderson Schneider, Xiaodong Liu, Jianfeng Gao

ICLR 2025 Towards Foundation Models for Mixed Integer Linear Programming Sirui Li, Janardhan Kulkarni, Ishai Menache, Cathy Wu, Beibin Li

ICLR 2024 Differentially Private Synthetic Data via Foundation Model APIs 1: Images Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin

ICLR 2024 Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation Xinyu Tang, Richard Shin, Huseyin A Inan, Andre Manoel, Fatemehsadat Mireshghallah, Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Robert Sim

ICLR 2024 Privately Aligning Language Models with Reinforcement Learning Fan Wu, Huseyin A Inan, Arturs Backurs, Varun Chandrasekaran, Janardhan Kulkarni, Robert Sim

TMLR 2024 Selective Pre-Training for Private Fine-Tuning Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang

NeurIPSW 2023 Differentially Private Synthetic Data via Foundation Model APIs 1: Images Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori, Sergey Yekhanin

ICLR 2023 Exploring the Limits of Differentially Private Deep Learning with Group-Wise Clipping Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian

TMLR 2023 Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent Da Yu, Gautam Kamath, Janardhan Kulkarni, Tie-Yan Liu, Jian Yin, Huishuai Zhang

NeurIPSW 2023 TinyGSM: Achieving 80% on GSM8k with One Billion Parameters Bingbin Liu, Sebastien Bubeck, Ronen Eldan, Janardhan Kulkarni, Yuanzhi Li, Anh Nguyen, Rachel Ward, Yi Zhang

NeurIPSW 2023 Training Private and Efficient Language Models with Synthetic Data from LLMs Da Yu, Arturs Backurs, Sivakanth Gopi, Huseyin Inan, Janardhan Kulkarni, Zinan Lin, Chulin Xie, Huishuai Zhang, Wanrong Zhang

ICLR 2022 Differentially Private Fine-Tuning of Language Models Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang

NeurIPS 2022 Differentially Private Model Compression FatemehSadat Mireshghallah, Arturs Backurs, Huseyin A. Inan, Lukas Wutschitz, Janardhan Kulkarni

NeurIPS 2022 When Does Differentially Private Learning Not Suffer in High Dimensions? Xuechen Li, Daogao Liu, Tatsunori B Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin-Tat Lee, Abhradeep Guha Thakurta

AISTATS 2021 Consistent K-Median: Simpler, Better and Robust Xiangyu Guo, Janardhan Kulkarni, Shi Li, Jiayi Xian

ICML 2021 Accuracy, Interpretability, and Differential Privacy via Explainable Boosting Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni

ICML 2021 Differentially Private Correlation Clustering Mark Bun, Marek Elias, Janardhan Kulkarni

NeurIPS 2021 Differentially Private N-Gram Extraction Kunho Kim, Sivakanth Gopi, Janardhan Kulkarni, Sergey Yekhanin

NeurIPS 2021 Fast and Memory Efficient Differentially Private-SGD via JL Projections Zhiqi Bu, Sivakanth Gopi, Janardhan Kulkarni, Yin Tat Lee, Hanwen Shen, Uthaipon Tantipongpipat

NeurIPS 2021 Private Non-Smooth ERM and SCO in Subquadratic Steps Janardhan Kulkarni, Yin Tat Lee, Daogao Liu

ICML 2020 Differentially Private Set Union Sivakanth Gopi, Pankaj Gulhane, Janardhan Kulkarni, Judy Hanwen Shen, Milad Shokouhi, Sergey Yekhanin

COLT 2020 Locally Private Hypothesis Selection Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni, Aleksandar Nikolov, Zhiwei Steven Wu, Huanyu Zhang

ICML 2020 Privately Learning Markov Random Fields Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni, Steven Wu

NeurIPS 2019 An Algorithmic Framework for Differentially Private Data Analysis on Trusted Processors Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin

NeurIPS 2019 Locally Private Gaussian Estimation Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Steven Z. Wu

NeurIPS 2017 Collecting Telemetry Data Privately Bolin Ding, Janardhan Kulkarni, Sergey Yekhanin