CYCLADES: Conflict-free Asynchronous Machine Learning. - Xinghao Pan; Maximilian Lam; Stephen Tu; Dimitris S. Papailiopoulos; Ce Zhang 001; Michael I. Jordan; Kannan Ramchandran; Christopher Ré; Benjamin Recht (CoRR, 2016)

Gradient Diversity Empowers Distributed Learning. - Dong Yin; Ashwin Pananjady; Maximilian Lam; Dimitris S. Papailiopoulos; Kannan Ramchandran; Peter L. Bartlett (CoRR, 2017)

Gradient Diversity: a Key Ingredient for Scalable Distributed Learning. - Dong Yin; Ashwin Pananjady; Maximilian Lam; Dimitris S. Papailiopoulos; Kannan Ramchandran; Peter L. Bartlett (AISTATS, 2018)

Cataloging the Visible Universe Through Bayesian Inference at Petascale. - Jeffrey Regier; Kiran Pamnany; Keno Fischer; Andreas Noack 001; Maximilian Lam; Jarrett Revels; Steve Howard; Ryan Giordano; David Schlegel; Jon McAuliffe; Rollin C. Thomas; Prabhat (IPDPS, 2018)

Exploring the Utility of Developer Exhaust. - Jian Zhang 049; Max Lam; Stephanie Wang; Paroma Varma; Luigi Nardi; Kunle Olukotun; Christopher Ré (DEEM@SIGMOD, 2018)

Word2Bits - Quantized Word Vectors. - Maximilian Lam (CoRR, 2018)

Speeding Up Distributed Machine Learning Using Codes. - Kangwook Lee 001; Maximilian Lam; Ramtin Pedarsani; Dimitris S. Papailiopoulos; Kannan Ramchandran (IEEE Trans. Inf. Theory, 2018)

Quantized Reinforcement Learning (QUARL). - Srivatsan Krishnan; Sharad Chitlangia; Maximilian Lam; Zishen Wan; Aleksandra Faust; Vijay Janapa Reddi (CoRR, 2019)

Cataloging the visible universe through Bayesian inference in Julia at petascale. - Jeffrey Regier; Keno Fischer; Kiran Pamnany; Andreas Noack 001; Jarrett Revels; Maximilian Lam; Steve Howard; Ryan Giordano; David Schlegel; Jon McAuliffe; Rollin C. Thomas; Prabhat (J. Parallel Distributed Comput., 2019)

RICOPILI: Rapid Imputation for COnsortias PIpeLIne. - Max Lam; Swapnil Awasthi; Hunna J. Watson; Jackie Goldstein; Georgia Panagiotaropoulou; Vassily Trubetskoy; Robert Karlsson; Oleksander Frei; Chun-Chieh Fan; Ward De Witte; Nina R. Mota; Niamh Mullins; et al. (Bioinform., 2020)

Quantized Neural Network Inference with Precision Batching. - Maximilian Lam; Zachary Yedidia; Colby R. Banbury; Vijay Janapa Reddi (CoRR, 2020)

Benchmarking TinyML Systems: Challenges and Direction. - Colby R. Banbury; Vijay Janapa Reddi; Max Lam; William Fu; Amin Fazel; Jeremy Holleman; Xinyuan Huang; Robert Hurtado; David Kanter; Anton Lokhmotov; David A. Patterson 001; Danilo Pau; et al. (CoRR, 2020)

Precision Batching: Bitserial Decomposition for Efficient Neural Network Inference on GPUs. - Maximilian Lam; Zachary Yedidia; Colby R. Banbury; Vijay Janapa Reddi (PACT, 2021)

Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. - Maximilian Lam; Gu-Yeon Wei; David Brooks 001; Vijay Janapa Reddi; Michael Mitzenmacher (ICML, 2021)

The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage. - Daniel Galvez; Greg Diamos; Juan Torres; Keith Achorn; Juan Felipe Cerón; Anjali Gopi; David Kanter; Max Lam; Mark Mazumder; Vijay Janapa Reddi (NeurIPS Datasets and Benchmarks, 2021)

Widening Access to Applied Machine Learning with TinyML. - 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; et al. (CoRR, 2021)

The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage. - Daniel Galvez; Greg Diamos; Juan Ciro; Juan Felipe Cerón; Keith Achorn; Anjali Gopi; David Kanter; Maximilian Lam; Mark Mazumder; Vijay Janapa Reddi (CoRR, 2021)

QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning. - Srivatsan Krishnan; Max Lam; Sharad Chitlangia; Zishen Wan; Gabriel Barth-Maron; Aleksandra Faust; Vijay Janapa Reddi (Trans. Mach. Learn. Res., 2022)

GWAS quality score for evaluating associated regions in GWAS analyses. - Swapnil Awasthi; Chia-Yen Chen; Max Lam; Hailiang Huang; Stephan Ripke; C. Anthony Altar (Bioinform., 2023)

GPU-based Private Information Retrieval for On-Device Machine Learning Inference. - Maximilian Lam; Jeff Johnson 004; Wenjie Xiong 001; Kiwan Maeng; Udit Gupta; Yang Li 183; Liangzhen Lai; Ilias Leontiadis; Minsoo Rhu; Hsien-Hsin S. Lee; Vijay Janapa Reddi; Gu-Yeon Wei; et al. (ASPLOS (1), 2024)

Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference. - Max Lam; Michael Mitzenmacher; Vijay Janapa Reddi; Gu-Yeon Wei; David Brooks 001 (Trans. Mach. Learn. Res., 2024)