Krishna Pillutla

40 papers A* 10A 2Misc 1Journal 23Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	An Inversion Theorem for Buffered Linear Toeplitz (BLT) Matrices and Applications to Streaming Differential Privacy. CoRR H. Brendan McMahan, Krishna Pillutla
2025	J	`jnl`	Correlated Noise Mechanisms for Differentially Private Learning. CoRR Krishna Pillutla, Jalaj Upadhyay, Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Arun Ganesh, Monika Henzinger, Jonathan Katz, Ryan McKenna, H. Brendan McMahan, Keith Rush, Thomas Steinke, Abhradeep Thakurta
2025	J	`jnl`	InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy. CoRR Vishnu Vinod, Krishna Pillutla, Abhradeep Guha Thakurta
2025	—	`conf`	Language Models can Subtly Deceive Without Lying: A Case Study on Strategic Phrasing in Legislation. ACL (1) Atharvan Dogra, Krishna Pillutla, Ameet Deshpande, Ananya B. Sai, John J. Nay, Tanmay Rajpurohit, Ashwin Kalyan, Balaraman Ravindran
2025	—	`conf`	Learning with User-Level Differential Privacy Under Fixed Compute Budgets. SaTML Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush
2024	A*	`conf`	Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. ICLR Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta
2024	A*	`conf`	Distributionally Robust Optimization with Bias and Variance Reduction. ICLR Ronak Mehta, Vincent Roulet, Krishna Pillutla, Zaïd Harchaoui
2024	A*	`conf`	Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. FOCS Krishnamurthy Dj Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta
2024	J	`jnl`	Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. CoRR Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta
2024	J	`jnl`	Federated learning with superquantile aggregation for heterogeneous data. Mach. Learn. Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaïd Harchaoui
2024	J	`jnl`	Fine-Tuning Large Language Models with User-Level Differential Privacy. CoRR Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush
2024	A*	`conf`	User Inference Attacks on Large Language Models. EMNLP Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu
2023	J	`jnl`	Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. CoRR Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta
2023	J	`jnl`	Distributionally Robust Optimization with Bias and Variance Reduction. CoRR Ronak Mehta, Vincent Roulet, Krishna Pillutla, Zaïd Harchaoui
2023	A	`conf`	Influence Diagnostics under Self-concordance. AISTATS Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaïd Harchaoui
2023	J	`jnl`	MAUVE Scores for Generative Models: Theory and Practice. J. Mach. Learn. Res. Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaïd Harchaoui
2023	—	`conf`	Modified Gauss-Newton Algorithms under Noise. SSP Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaïd Harchaoui
2023	J	`jnl`	Modified Gauss-Newton Algorithms under Noise. CoRR Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaïd Harchaoui
2023	A	`conf`	Stochastic Optimization for Spectral Risk Measures. AISTATS Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaïd Harchaoui
2023	A*	`conf`	Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. NeurIPS Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett
2023	J	`jnl`	Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. CoRR Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett
2023	A*	`conf`	Unleashing the Power of Randomization in Auditing Differentially Private ML. NeurIPS Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh
2023	J	`jnl`	Unleashing the Power of Randomization in Auditing Differentially Private ML. CoRR Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh
2023	J	`jnl`	User Inference Attacks on Large Language Models. CoRR Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu
2022	A*	`conf`	Federated Learning with Partial Model Personalization. ICML Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael G. Rabbat, Maziar Sanjabi, Lin Xiao
2022	J	`jnl`	Federated Learning with Partial Model Personalization. CoRR Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael G. Rabbat, Maziar Sanjabi, Lin Xiao
2022	—	`—`	From Enormous Structured Models to On-device Federated Learning: Robustness, Heterogeneity and Optimization Krishna Pillutla
2022	J	`jnl`	MAUVE Scores for Generative Models: Theory and Practice. CoRR Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaïd Harchaoui
2022	J	`jnl`	Robust Aggregation for Federated Learning. IEEE Trans. Signal Process. Krishna Pillutla, Sham M. Kakade, Zaïd Harchaoui
2022	J	`jnl`	Statistical and Computational Guarantees for Influence Diagnostics. CoRR Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaïd Harchaoui
2022	J	`jnl`	Stochastic Optimization for Spectral Risk Measures. CoRR Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaïd Harchaoui
2021	Misc	`conf`	A Superquantile Approach to Federated Learning with Heterogeneous Devices. CISS Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaïd Harchaoui
2021	A*	`conf`	Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals. NeurIPS Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaïd Harchaoui
2021	J	`jnl`	Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral. CoRR Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaïd Harchaoui
2021	J	`jnl`	Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach. CoRR Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaïd Harchaoui
2021	A*	`conf`	LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. NeurIPS Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham M. Kakade, Ali Farhadi
2021	J	`jnl`	LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. CoRR Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham M. Kakade, Ali Farhadi
2021	J	`jnl`	MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation. CoRR Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Yejin Choi, Zaïd Harchaoui
2021	A*	`conf`	MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers. NeurIPS Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, Zaïd Harchaoui
2020	J	`jnl`	Device Heterogeneity in Federated Learning: A Superquantile Approach. CoRR Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaïd Harchaoui