Maximilian Lam

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 19

Top Topics

Machine Learning

Top Venues

IEEE Trans. Inf. Theory

Publications

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
GPU-based Private Information Retrieval for On-Device Machine Learning Inference. ASPLOS (1) (2024)
Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Minsoo Rhu, Hsien-Hsin S. Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks, G. Edward Suh
GPU-based Private Information Retrieval for On-Device Machine Learning Inference. CoRR (2023)
Maximilian Lam, Michael Mitzenmacher, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference. CoRR (2022)
Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. ICML (2021)
Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. CoRR (2021)
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
Widening Access to Applied Machine Learning with TinyML. CoRR (2021)
Maximilian Lam, Zachary Yedidia, Colby R. Banbury, Vijay Janapa Reddi
Precision Batching: Bitserial Decomposition for Efficient Neural Network Inference on GPUs. PACT (2021)
Daniel Galvez, Greg Diamos, Juan Ciro, Juan Felipe Cerón, Keith Achorn, Anjali Gopi, David Kanter, Maximilian Lam, Mark Mazumder, Vijay Janapa Reddi
The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage. CoRR (2021)
Maximilian Lam, Zachary Yedidia, Colby R. Banbury, Vijay Janapa Reddi
Quantized Neural Network Inference with Precision Batching. CoRR (2020)
Srivatsan Krishnan, Sharad Chitlangia, Maximilian Lam, Zishen Wan, Aleksandra Faust, Vijay Janapa Reddi
Quantized Reinforcement Learning (QUARL). CoRR (2019)
Jeffrey Regier, Keno Fischer, Kiran Pamnany, Andreas Noack, Jarrett Revels, Maximilian Lam, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat
Cataloging the visible universe through Bayesian inference in Julia at petascale. J. Parallel Distributed Comput. 127 (2019)
Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat
Cataloging the Visible Universe through Bayesian Inference at Petascale. CoRR (2018)
Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning. AISTATS (2018)
Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat
Cataloging the Visible Universe Through Bayesian Inference at Petascale. IPDPS (2018)
Maximilian Lam
Word2Bits - Quantized Word Vectors. CoRR (2018)
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran
Speeding Up Distributed Machine Learning Using Codes. IEEE Trans. Inf. Theory 64 (3) (2018)
Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett
Gradient Diversity Empowers Distributed Learning. CoRR (2017)
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran
Speeding up distributed machine learning using codes. ISIT (2016)
Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris S. Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré, Benjamin Recht
CYCLADES: Conflict-free Asynchronous Machine Learning. CoRR (2016)
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran
Speeding Up Distributed Machine Learning Using Codes. CoRR (2015)