Ameet Talwalkar
Ameet Talwalkar
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Cited by
Cited by
Foundations of Machine Learning
M Mohri, A Rostamizadeh, A Talwalkar
Cambridge, MA: MIT Press, 2018
Federated learning: Challenges, methods, and future directions
T Li, AK Sahu, A Talwalkar, V Smith
IEEE signal processing magazine 37 (3), 50-60, 2020
Federated optimization in heterogeneous networks
T Li, AK Sahu, M Zaheer, M Sanjabi, A Talwalkar, V Smith
Proceedings of Machine learning and systems 2, 429-450, 2020
Hyperband: A novel bandit-based approach to hyperparameter optimization
L Li, K Jamieson, G DeSalvo, A Rostamizadeh, A Talwalkar
arXiv preprint arXiv:1603.06560, 2016
Mllib: Machine learning in apache spark
X Meng, J Bradley, B Yavuz, E Sparks, S Venkataraman, D Liu, ...
Journal of Machine Learning Research 17 (34), 1-7, 2016
Federated multi-task learning
V Smith, CK Chiang, M Sanjabi, AS Talwalkar
Advances in neural information processing systems 30, 2017
Leaf: A benchmark for federated settings
S Caldas, SMK Duddu, P Wu, T Li, J Konečnı, HB McMahan, V Smith, ...
arXiv preprint arXiv:1812.01097, 2018
A large-scale evaluation of computational protein function prediction
P Radivojac, WT Clark, TR Oron, AM Schnoes, T Wittkop, A Sokolov, ...
Nature methods 10 (3), 221-227, 2013
Random search and reproducibility for neural architecture search
L Li, A Talwalkar
Uncertainty in artificial intelligence, 367-377, 2020
Non-stochastic best arm identification and hyperparameter optimization
K Jamieson, A Talwalkar
Artificial intelligence and statistics, 240-248, 2016
A scalable bootstrap for massive data
A Kleiner, A Talwalkar, P Sarkar, MI Jordan
Journal of the Royal Statistical Society, Series B, 2013
MLbase: A Distributed Machine-learning System.
T Kraska, A Talwalkar, JC Duchi, R Griffith, MJ Franklin, MI Jordan
Cidr 1, 2-1, 2013
Sampling methods for the Nyström method
S Kumar, M Mohri, A Talwalkar
The Journal of Machine Learning Research 13 (1), 981-1006, 2012
Expanding the reach of federated learning by reducing client resource requirements
S Caldas, J Konečny, HB McMahan, A Talwalkar
arXiv preprint arXiv:1812.07210, 2018
A system for massively parallel hyperparameter tuning
L Li, K Jamieson, A Rostamizadeh, E Gonina, J Ben-Tzur, M Hardt, ...
Proceedings of Machine Learning and Systems 2, 230-246, 2020
Adaptive gradient-based meta-learning methods
M Khodak, MFF Balcan, AS Talwalkar
Advances in Neural Information Processing Systems 32, 2019
A field guide to federated optimization
J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ...
arXiv preprint arXiv:2107.06917, 2021
Large-scale manifold learning
A Talwalkar, S Kumar, H Rowley
2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008
MLI: An API for distributed machine learning
ER Sparks, A Talwalkar, V Smith, J Kottalam, X Pan, J Gonzalez, ...
2013 IEEE 13th International Conference on Data Mining, 1187-1192, 2013
Divide-and-Conquer Matrix Factorization
LW Mackey, A Talwalkar, MI Jordan
NIPS, 1134-1142, 2011
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