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MAUVE Scores for Generative Models: Theory and Practice


Dec 30, 2022
Krishna Pillutla, Lang Liu, John Thickstun, Sean Welleck, Swabha Swayamdipta, Rowan Zellers, Sewoong Oh, Yejin Choi, Zaid Harchaoui

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Stochastic Optimization for Spectral Risk Measures


Dec 10, 2022
Ronak Mehta, Vincent Roulet, Krishna Pillutla, Lang Liu, Zaid Harchaoui

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Statistical and Computational Guarantees for Influence Diagnostics


Dec 08, 2022
Jillian Fisher, Lang Liu, Krishna Pillutla, Yejin Choi, Zaid Harchaoui

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Federated Learning with Partial Model Personalization


Apr 08, 2022
Krishna Pillutla, Kshitiz Malik, Abdelrahman Mohamed, Michael Rabbat, Maziar Sanjabi, Lin Xiao

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Federated Learning with Heterogeneous Data: A Superquantile Optimization Approach


Dec 17, 2021
Krishna Pillutla, Yassine Laguel, J茅r么me Malick, Zaid Harchaoui

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* This is the longer version of a conference paper published in IEEE CISS 2021 

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Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral


Jun 15, 2021
Lang Liu, Krishna Pillutla, Sean Welleck, Sewoong Oh, Yejin Choi, Zaid Harchaoui

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LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes


Jun 02, 2021
Aditya Kusupati, Matthew Wallingford, Vivek Ramanujan, Raghav Somani, Jae Sung Park, Krishna Pillutla, Prateek Jain, Sham Kakade, Ali Farhadi

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* 18 pages, 6 figures 

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


Feb 02, 2021
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Yejin Choi, Zaid Harchaoui

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Device Heterogeneity in Federated Learning: A Superquantile Approach


Feb 25, 2020
Yassine Laguel, Krishna Pillutla, J茅r么me Malick, Zaid Harchaoui

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Robust Aggregation for Federated Learning


Dec 31, 2019
Krishna Pillutla, Sham M. Kakade, Zaid Harchaoui

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