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ORCIDKrishna Pillutla
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2020 – today
- 2024
[j3]Krishna Pillutla
, Yassine Laguel, Jérôme Malick, Zaïd Harchaoui:
Federated learning with superquantile aggregation for heterogeneous data. Mach. Learn. 113(5): 2955-3022 (2024)- [c12]Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta:
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. ICLR 2024 - [c11]Ronak Mehta, Vincent Roulet, Krishna Pillutla, Zaïd Harchaoui:
Distributionally Robust Optimization with Bias and Variance Reduction. ICLR 2024 - [i17]Krishnamurthy Dvijotham, H. Brendan McMahan, Krishna Pillutla, Thomas Steinke, Abhradeep Thakurta:
Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. CoRR abs/2404.16706 (2024) - [i16]Zachary Charles, Arun Ganesh, Ryan McKenna, H. Brendan McMahan, Nicole Mitchell, Krishna Pillutla, Keith Rush:
Fine-Tuning Large Language Models with User-Level Differential Privacy. CoRR abs/2407.07737 (2024) - 2023
- [j2]Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaïd Harchaoui:
MAUVE Scores for Generative Models: Theory and Practice. J. Mach. Learn. Res. 24: 356:1-356:92 (2023) - [c10]Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaïd Harchaoui:
Influence Diagnostics under Self-concordance. AISTATS 2023: 10028-10076 - [c9]Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaïd Harchaoui:
Stochastic Optimization for Spectral Risk Measures. AISTATS 2023: 10112-10159 - [c8]Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett:
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. NeurIPS 2023 - [c7]Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh:
Unleashing the Power of Randomization in Auditing Differentially Private ML. NeurIPS 2023 - [c6]Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaïd Harchaoui:
Modified Gauss-Newton Algorithms under Noise. SSP 2023: 51-55 - [i15]Krishna Pillutla, Vincent Roulet, Sham M. Kakade, Zaïd Harchaoui:
Modified Gauss-Newton Algorithms under Noise. CoRR abs/2305.10634 (2023) - [i14]Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh:
Unleashing the Power of Randomization in Auditing Differentially Private ML. CoRR abs/2305.18447 (2023) - [i13]Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett:
Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. CoRR abs/2307.09619 (2023) - [i12]Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Thakurta:
Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. CoRR abs/2310.06771 (2023) - [i11]Nikhil Kandpal, Krishna Pillutla, Alina Oprea, Peter Kairouz, Christopher A. Choquette-Choo, Zheng Xu:
User Inference Attacks on Large Language Models. CoRR abs/2310.09266 (2023) - [i10]Ronak Mehta, Vincent Roulet, Krishna Pillutla, Zaïd Harchaoui:
Distributionally Robust Optimization with Bias and Variance Reduction. CoRR abs/2310.13863 (2023) - 2022
- [b1]Krishna Pillutla:
From Enormous Structured Models to On-device Federated Learning: Robustness, Heterogeneity and Optimization. University of Washington, USA, 2022 - [j1]Krishna Pillutla, Sham M. Kakade, Zaïd Harchaoui:
Robust Aggregation for Federated Learning. IEEE Trans. Signal Process. 70: 1142-1154 (2022) - [c5]Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael G. Rabbat, Maziar Sanjabi, Lin Xiao:
Federated Learning with Partial Model Personalization. ICML 2022: 17716-17758 - [i9]Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael G. Rabbat, Maziar Sanjabi, Lin Xiao:
Federated Learning with Partial Model Personalization. CoRR abs/2204.03809 (2022) - [i8]Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaïd Harchaoui:
Statistical and Computational Guarantees for Influence Diagnostics. CoRR abs/2212.04014 (2022) - [i7]Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaïd Harchaoui:
Stochastic Optimization for Spectral Risk Measures. CoRR abs/2212.05149 (2022) - [i6]Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaïd Harchaoui:
MAUVE Scores for Generative Models: Theory and Practice. CoRR abs/2212.14578 (2022) - 2021
- [c4]Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaïd Harchaoui:
A Superquantile Approach to Federated Learning with Heterogeneous Devices. CISS 2021: 1-6 - [c3]Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, Zaïd Harchaoui:
MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers. NeurIPS 2021: 4816-4828 - [c2]Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaïd Harchaoui:
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals. NeurIPS 2021: 12930-12942 - [c1]Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham M. Kakade, Ali Farhadi:
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. NeurIPS 2021: 23900-23913 - [i5]Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Yejin Choi, Zaïd Harchaoui:
MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation. CoRR abs/2102.01454 (2021) - [i4]Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham M. Kakade, Ali Farhadi:
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes. CoRR abs/2106.01487 (2021) - [i3]Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaïd Harchaoui:
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral. CoRR abs/2106.07898 (2021) - [i2]Krishna Pillutla, Yassine Laguel, Jérôme Malick, Zaïd Harchaoui:
Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach. CoRR abs/2112.09429 (2021) - 2020
- [i1]Yassine Laguel, Krishna Pillutla, Jérôme Malick, Zaïd Harchaoui:
Device Heterogeneity in Federated Learning: A Superquantile Approach. CoRR abs/2002.11223 (2020)
Coauthor Index
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