Pillutla, Krishna

20 publications

NeurIPS 2025 InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy Vishnu Vinod, Krishna Pillutla, Abhradeep Guha Thakurta

ICLR 2024 Correlated Noise Provably Beats Independent Noise for Differentially Private Learning Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta

ICLR 2024 Distributionally Robust Optimization with Bias and Variance Reduction Ronak Mehta, Vincent Roulet, Krishna Pillutla, Zaid Harchaoui

MLJ 2024 Federated Learning with Superquantile Aggregation for Heterogeneous Data Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaïd Harchaoui

ICMLW 2024 Fine-Tuning Large Language Models with User-Level Differential Privacy Zachary Charles, Arun Ganesh, Ryan McKenna, Hugh Brendan McMahan, Nicole Elyse Mitchell, Krishna Pillutla, J Keith Rush

NeurIPSW 2023 Correlated Noise Provably Beats Independent Noise for Differentially Private Learning Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta

AISTATS 2023 Influence Diagnostics Under Self-Concordance Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaid Harchaoui

JMLR 2023 MAUVE Scores for Generative Models: Theory and Practice Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaid Harchaoui

AISTATS 2023 Stochastic Optimization for Spectral Risk Measures Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaid Harchaoui

NeurIPS 2023 Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett

NeurIPS 2023 Unleashing the Power of Randomization in Auditing Differentially Private ML Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh

ICMLW 2023 Unleashing the Power of Randomization in Auditing Differentially Private ML Krishna Pillutla, Galen Andrew, Peter Kairouz, Hugh Brendan McMahan, Alina Oprea, Sewoong Oh

NeurIPSW 2023 User Inference Attacks on LLMs Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher Choquette-Choo, Zheng Xu

NeurIPSW 2023 User Inference Attacks on Large Language Models Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu

NeurIPSW 2022 Differentially Private Federated Quantiles with the Distributed Discrete Gaussian Mechanism Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaid Harchaoui

ICML 2022 Federated Learning with Partial Model Personalization Krishna Pillutla, Kshitiz Malik, Abdel-Rahman Mohamed, Mike Rabbat, Maziar Sanjabi, Lin Xiao

NeurIPSW 2022 Tackling Distribution Shifts in Federated Learning with Superquantile Aggregation Krishna Pillutla, Yassine Laguel, Jerome Malick, Zaid Harchaoui

NeurIPS 2021 Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaid Harchaoui

NeurIPS 2021 LLC: Accurate, Multi-Purpose Learnt Low-Dimensional Binary Codes Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham Kakade, Ali Farhadi

NeurIPS 2021 MAUVE: Measuring the Gap Between Neural Text and Human Text Using Divergence Frontiers Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, Zaid Harchaoui