| Speeding up distributed machine learning using codes K Lee, M Lam, R Pedarsani, D Papailiopoulos, K Ramchandran IEEE Transactions on Information Theory 64 (3), 1514-1529, 2017 | 868 | 2017 |
| Benchmarking tinyml systems: Challenges and direction CR Banbury, VJ Reddi, M Lam, W Fu, A Fazel, J Holleman, X Huang, ... arXiv preprint arXiv:2003.04821, 2020 | 218 | 2020 |
| Gradient diversity: a key ingredient for scalable distributed learning D Yin, A Pananjady, M Lam, D Papailiopoulos, K Ramchandran, P Bartlett Proceedings of the 21th International Conference on Artificial Intelligence …, 2017 | 140 | 2017 |
| Cyclades: Conflict-free asynchronous machine learning X Pan, M Lam, S Tu, D Papailiopoulos, C Zhang, MI Jordan, ... Advances in Neural Information Processing Systems 29, 2016 | 65 | 2016 |
| Widening access to applied machine learning with tinyml VJ Reddi, B Plancher, S Kennedy, L Moroney, P Warden, A Agarwal, ... arXiv preprint arXiv:2106.04008, 2021 | 46 | 2021 |
| The people's speech: A large-scale diverse english speech recognition dataset for commercial usage D Galvez, G Diamos, J Ciro, JF Cerón, K Achorn, A Gopi, D Kanter, M Lam, ... arXiv preprint arXiv:2111.09344, 2021 | 42 | 2021 |
| Cataloging the visible universe through Bayesian inference in Julia at petascale J Regier, K Fischer, K Pamnany, A Noack, J Revels, M Lam, S Howard, ... Journal of Parallel and Distributed Computing 127, 89-104, 2019 | 42 | 2019 |
| Gradient disaggregation: Breaking privacy in federated learning by reconstructing the user participant matrix M Lam, GY Wei, D Brooks, VJ Reddi, M Mitzenmacher International Conference on Machine Learning, 5959-5968, 2021 | 41 | 2021 |
| Quantized reinforcement learning (quarl) S Krishnan, S Chitlangia, M Lam, Z Wan, A Faust, VJ Reddi arXiv preprint arXiv:1910.01055, 2019 | 36* | 2019 |
| Word2bits-quantized word vectors M Lam arXiv preprint arXiv:1803.05651, 2018 | 27 | 2018 |
| GPU-based Private Information Retrieval for On-Device Machine Learning Inference M Lam, J Johnson, W Xiong, K Maeng, U Gupta, M Rhu, HHS Lee, ... arXiv preprint arXiv:2301.10904, 2023 | 4 | 2023 |
| Quantized neural network inference with precision batching M Lam, Z Yedidia, C Banbury, VJ Reddi arXiv preprint arXiv:2003.00822, 2020 | 3 | 2020 |
| Tabula: Efficiently computing nonlinear activation functions for secure neural network inference M Lam, M Mitzenmacher, VJ Reddi, GY Wei, D Brooks arXiv preprint arXiv:2203.02833, 2022 | 2 | 2022 |
| Exploring the Utility of Developer Exhaust J Zhang, M Lam, S Wang, P Varma, L Nardi, K Olukotun, C Ré Proceedings of the Second Workshop on Data Management for End-To-End Machine …, 2018 | 1 | 2018 |
