Device Heterogeneity in Federated Learning: A Superquantile Approach. - Yassine Laguel; Krishna Pillutla; Jérôme Malick; Zaïd Harchaoui (CoRR, 2020)

A Superquantile Approach to Federated Learning with Heterogeneous Devices. - Yassine Laguel; Krishna Pillutla; Jérôme Malick; Zaïd Harchaoui (CISS, 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 002; Sham M. Kakade; Ali Farhadi (NeurIPS, 2021)

Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals. - Lang Liu; Krishna Pillutla; Sean Welleck; Sewoong Oh; Yejin Choi 001; Zaïd Harchaoui (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 001; Zaïd Harchaoui (NeurIPS, 2021)

MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation. - Krishna Pillutla; Swabha Swayamdipta; Rowan Zellers; John Thickstun; Yejin Choi 001; Zaïd Harchaoui (CoRR, 2021)

Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral. - Lang Liu; Krishna Pillutla; Sean Welleck; Sewoong Oh; Yejin Choi 001; Zaïd Harchaoui (CoRR, 2021)

Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach. - Krishna Pillutla; Yassine Laguel; Jérôme Malick; Zaïd Harchaoui (CoRR, 2021)

Federated Learning with Partial Model Personalization. - Krishna Pillutla; Kshitiz Malik; Abdelrahman Mohamed; Michael G. Rabbat; Maziar Sanjabi; Lin Xiao 003 (ICML, 2022)

Statistical and Computational Guarantees for Influence Diagnostics. - Jillian Fisher; Lang Liu; Krishna Pillutla; Yejin Choi 001; Zaïd Harchaoui (CoRR, 2022)

Robust Aggregation for Federated Learning. - Krishna Pillutla; Sham M. Kakade; Zaïd Harchaoui (IEEE Trans. Signal Process., 2022)

From Enormous Structured Models to On-device Federated Learning: Robustness, Heterogeneity and Optimization - Krishna Pillutla (2022)

Influence Diagnostics under Self-concordance. - Jillian Fisher; Lang Liu; Krishna Pillutla; Yejin Choi 001; Zaïd Harchaoui (AISTATS, 2023)

Stochastic Optimization for Spectral Risk Measures. - Ronak Mehta; Vincent Roulet; Krishna Pillutla; Lang Liu; Zaïd Harchaoui (AISTATS, 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 (NeurIPS, 2023)

Modified Gauss-Newton Algorithms under Noise. - Krishna Pillutla; Vincent Roulet; Sham M. Kakade; Zaïd Harchaoui (SSP, 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 001; Zaïd Harchaoui (J. Mach. Learn. Res., 2023)

User Inference Attacks on Large Language Models. - Nikhil Kandpal; Krishna Pillutla; Alina Oprea; Peter Kairouz; Christopher A. Choquette-Choo; Zheng Xu 002 (EMNLP, 2024)

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. - Krishnamurthy Dj Dvijotham; H. Brendan McMahan; Krishna Pillutla; Thomas Steinke 001; Abhradeep Thakurta (FOCS, 2024)

Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. - Christopher A. Choquette-Choo; Krishnamurthy Dj Dvijotham; Krishna Pillutla; Arun Ganesh; Thomas Steinke 002; Abhradeep Guha Thakurta (ICLR, 2024)

Distributionally Robust Optimization with Bias and Variance Reduction. - Ronak Mehta; Vincent Roulet; Krishna Pillutla; Zaïd Harchaoui (ICLR, 2024)

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. - Krishnamurthy Dvijotham; H. Brendan McMahan; Krishna Pillutla; Thomas Steinke 002; Abhradeep Thakurta (CoRR, 2024)

Fine-Tuning Large Language Models with User-Level Differential Privacy. - Zachary Charles; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Nicole Mitchell; Krishna Pillutla; Keith Rush (CoRR, 2024)

Federated learning with superquantile aggregation for heterogeneous data. - Krishna Pillutla; Yassine Laguel; Jérôme Malick; Zaïd Harchaoui (Mach. Learn., 2024)

Language Models can Subtly Deceive Without Lying: A Case Study on Strategic Phrasing in Legislation. - Atharvan Dogra; Krishna Pillutla; Ameet Deshpande; Ananya B. Sai; John J. Nay; Tanmay Rajpurohit; Ashwin Kalyan; Balaraman Ravindran (ACL (1), 2025)

Learning with User-Level Differential Privacy Under Fixed Compute Budgets. - Zachary Charles; Arun Ganesh; Ryan McKenna; H. Brendan McMahan; Nicole Mitchell; Krishna Pillutla; Keith Rush (SaTML, 2025)

An Inversion Theorem for Buffered Linear Toeplitz (BLT) Matrices and Applications to Streaming Differential Privacy. - H. Brendan McMahan; Krishna Pillutla (CoRR, 2025)

Correlated Noise Mechanisms for Differentially Private Learning. - 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 002; Abhradeep Thakurta (CoRR, 2025)

InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy. - Vishnu Vinod; Krishna Pillutla; Abhradeep Guha Thakurta (CoRR, 2025)