Maximilian Lam

20 papers A* 1A 2B 2Journal 14Unranked 1

Year	Rank	Type	Title / Venue / Authors
2024	—	`conf`	GPU-based Private Information Retrieval for On-Device Machine Learning Inference. ASPLOS (1) Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Yang Li, Liangzhen Lai, Ilias Leontiadis, Minsoo Rhu, Hsien-Hsin S. Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks, G. Edward Suh
2023	J	`jnl`	GPU-based Private Information Retrieval for On-Device Machine Learning Inference. CoRR Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Yang Li, Liangzhen Lai, Ilias Leontiadis, Minsoo Rhu, Hsien-Hsin S. Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks, G. Edward Suh
2022	J	`jnl`	Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference. CoRR Maximilian Lam, Michael Mitzenmacher, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks
2021	A*	`conf`	Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. ICML Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher
2021	J	`jnl`	Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. CoRR Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher
2021	B	`conf`	Precision Batching: Bitserial Decomposition for Efficient Neural Network Inference on GPUs. PACT Maximilian Lam, Zachary Yedidia, Colby R. Banbury, Vijay Janapa Reddi
2021	J	`jnl`	The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage. CoRR Daniel Galvez, Greg Diamos, Juan Ciro, Juan Felipe Cerón, Keith Achorn, Anjali Gopi, David Kanter, Maximilian Lam, Mark Mazumder, Vijay Janapa Reddi
2021	J	`jnl`	Widening Access to Applied Machine Learning with TinyML. CoRR Vijay Janapa Reddi, Brian Plancher, Susan Kennedy, Laurence Moroney, Pete Warden, Anant Agarwal, Colby R. 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
2020	J	`jnl`	Quantized Neural Network Inference with Precision Batching. CoRR Maximilian Lam, Zachary Yedidia, Colby R. Banbury, Vijay Janapa Reddi
2019	J	`jnl`	Cataloging the visible universe through Bayesian inference in Julia at petascale. J. Parallel Distributed Comput. Jeffrey Regier, Keno Fischer, Kiran Pamnany, Andreas Noack, Jarrett Revels, Maximilian Lam, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat
2019	J	`jnl`	Quantized Reinforcement Learning (QUARL). CoRR Srivatsan Krishnan, Sharad Chitlangia, Maximilian Lam, Zishen Wan, Aleksandra Faust, Vijay Janapa Reddi
2018	A	`conf`	Cataloging the Visible Universe Through Bayesian Inference at Petascale. IPDPS Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat
2018	J	`jnl`	Cataloging the Visible Universe through Bayesian Inference at Petascale. CoRR Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat
2018	A	`conf`	Gradient Diversity: a Key Ingredient for Scalable Distributed Learning. AISTATS Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett
2018	J	`jnl`	Speeding Up Distributed Machine Learning Using Codes. IEEE Trans. Inf. Theory Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran
2018	J	`jnl`	Word2Bits - Quantized Word Vectors. CoRR Maximilian Lam
2017	J	`jnl`	Gradient Diversity Empowers Distributed Learning. CoRR Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett
2016	J	`jnl`	CYCLADES: Conflict-free Asynchronous Machine Learning. CoRR Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris S. Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré, Benjamin Recht
2016	B	`conf`	Speeding up distributed machine learning using codes. ISIT Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran
2015	J	`jnl`	Speeding Up Distributed Machine Learning Using Codes. CoRR Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran