Krishna Pillutla

According to our database, Krishna Pillutla authored at least 25 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Federated learning with superquantile aggregation for heterogeneous data.

Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaïd Harchaoui

Mach. Learn., May, 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

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy.

Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta

CoRR, 2024

Distributionally Robust Optimization with Bias and Variance Reduction.

Ronak Mehta, Vincent Roulet, Krishna Pillutla, Zaïd Harchaoui

Proceedings of the Twelfth International Conference on Learning Representations, 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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy.

Krishnamurthy Dj Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta

Proceedings of the 65th IEEE Annual Symposium on Foundations of Computer Science, 2024

User Inference Attacks on Large Language Models.

Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

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, Zaïd Harchaoui

J. Mach. Learn. Res., 2023

Modified Gauss-Newton Algorithms under Noise.

Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaïd Harchaoui

Proceedings of the IEEE Statistical Signal Processing Workshop, 2023

Unleashing the Power of Randomization in Auditing Differentially Private ML.

Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning.

Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Stochastic Optimization for Spectral Risk Measures.

Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaïd Harchaoui

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Influence Diagnostics under Self-concordance.

Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaïd Harchaoui

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

From Enormous Structured Models to On-device Federated Learning: Robustness, Heterogeneity and Optimization

Krishna Pillutla

PhD thesis, 2022

Robust Aggregation for Federated Learning.

Krishna Pillutla, Sham M. Kakade, Zaïd Harchaoui

IEEE Trans. Signal Process., 2022

Statistical and Computational Guarantees for Influence Diagnostics.

Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaïd Harchaoui

CoRR, 2022

Federated Learning with Partial Model Personalization.

Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael G. Rabbat, Maziar Sanjabi, Lin Xiao

Proceedings of the International Conference on Machine Learning, 2022

2021

Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach.

Krishna Pillutla, Yassine Laguel, Jérôme Malick, 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, Zaïd Harchaoui

CoRR, 2021

MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation.

Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Yejin Choi, Zaïd Harchaoui

CoRR, 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, Zaïd Harchaoui

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals.

Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaïd Harchaoui

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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 M. Kakade, Ali Farhadi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Superquantile Approach to Federated Learning with Heterogeneous Devices.

Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaïd Harchaoui

Proceedings of the 55th Annual Conference on Information Sciences and Systems, 2021

2020

Device Heterogeneity in Federated Learning: A Superquantile Approach.

Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaïd Harchaoui

CoRR, 2020