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Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix


Jun 10, 2021
Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher

* ICML 2021 

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Widening Access to Applied Machine Learning with TinyML


Jun 09, 2021
Vijay Janapa Reddi, Brian Plancher, Susan Kennedy, Laurence Moroney, Pete Warden, Anant Agarwal, Colby Banbury, Massimo Banzi, Matthew Bennett, Benjamin Brown, Sharad Chitlangia, Radhika Ghosal, Sarah Grafman, Rupert Jaeger, Srivatsan Krishnan, Maximilian Lam, Daniel Leiker, Cara Mann, Mark Mazumder, Dominic Pajak, Dhilan Ramaprasad, J. Evan Smith, Matthew Stewart, Dustin Tingley

* Understanding the underpinnings of the TinyML edX course series: https://www.edx.org/professional-certificate/harvardx-tiny-machine-learning 

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Quantized Neural Network Inference with Precision Batching


Feb 26, 2020
Maximilian Lam, Zachary Yedidia, Colby Banbury, Vijay Janapa Reddi


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Quantized Reinforcement Learning (QUARL)


Oct 04, 2019
Srivatsan Krishnan, Sharad Chitlangia, Maximilian Lam, Zishen Wan, Aleksandra Faust, Vijay Janapa Reddi

* Equal contribution from first three authors 

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Word2Bits - Quantized Word Vectors


Mar 31, 2018
Maximilian Lam


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Speeding Up Distributed Machine Learning Using Codes


Jan 29, 2018
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, Kannan Ramchandran

* This work is published in IEEE Transactions on Information Theory and presented in part at the NIPS 2015 Workshop on Machine Learning Systems and the IEEE ISIT 2016 

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CYCLADES: Conflict-free Asynchronous Machine Learning


May 31, 2016
Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Chris Re, Benjamin Recht


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