Ameet Talwalkar

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Virginia SmithCarnegie Mellon UniversityVerified email at cmu.edu
Mehryar MohriHead, Learning Theory Team, Google Research; Professor, Courant Institute of Mathematical Sciences.Verified email at google.com
Liam LiCarnegie Mellon UniversityVerified email at liamcli.com
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyVerified email at cs.berkeley.edu
Mikhail KhodakCMUVerified email at cs.princeton.edu
Tian LiUniversity of ChicagoVerified email at uchicago.edu
Kevin JamiesonAssociate Professor, University of WashingtonVerified email at cs.washington.edu
Afshin RostamizadehGoogle ResearchVerified email at google.com
Anit Kumar SahuAmazonVerified email at amazon.com
Michael FranklinLiew Family Chair of Computer Science, University of ChicagoVerified email at uchicago.edu
Maria-Florina BalcanProfessor, School of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Maziar SanjabiMeta AIVerified email at fb.com
Gregory PlumbCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Sanjiv KumarGoogle Fellow, VP, Google ResearchVerified email at google.com
Giulia DeSalvoGoogle ResearchVerified email at GOOGLE.COM
Matei ZahariaUC Berkeley and DatabricksVerified email at berkeley.edu
Sebastian CaldasPh.D. Student, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Valerie ChenCarnegie Mellon UniversityVerified email at cmu.edu
Manzil ZaheerGoogle ResearchVerified email at cmu.edu
Lester MackeyMicrosoft ResearchVerified email at microsoft.com

Ameet Talwalkar

CMU

Verified email at cmu.edu - Homepage

Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Foundations of Machine Learning M Mohri, A Rostamizadeh, A Talwalkar Cambridge, MA: MIT Press, 2018	6725	2018
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	5245	2020
Federated learning: Challenges, methods, and future directions T Li, AK Sahu, A Talwalkar, V Smith IEEE signal processing magazine 37 (3), 50-60, 2020	5113	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	2892	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	2436	2016
Federated multi-task learning V Smith, CK Chiang, M Sanjabi, AS Talwalkar Advances in neural information processing systems 30, 2017	2078	2017
Leaf: A benchmark for federated settings S Caldas, SMK Duddu, P Wu, T Li, J Konečný, HB McMahan, V Smith, ... arXiv preprint arXiv:1812.01097, 2018	1423	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	1088	2013
Random search and reproducibility for neural architecture search L Li, A Talwalkar Uncertainty in artificial intelligence, 367-377, 2020	808	2020
Non-stochastic best arm identification and hyperparameter optimization K Jamieson, A Talwalkar Artificial intelligence and statistics, 240-248, 2016	725	2016
A scalable bootstrap for massive data A Kleiner, A Talwalkar, P Sarkar, MI Jordan Journal of the Royal Statistical Society, Series B, 2013	496	2013
MLbase: A Distributed Machine-learning System. T Kraska, A Talwalkar, JC Duchi, R Griffith, MJ Franklin, MI Jordan Cidr 1, 2-1, 2013	477	2013
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	474	2018
Sampling methods for the Nyström method S Kumar, M Mohri, A Talwalkar The Journal of Machine Learning Research 13 (1), 981-1006, 2012	468	2012
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	443	2020
Adaptive gradient-based meta-learning methods M Khodak, MFF Balcan, AS Talwalkar Advances in Neural Information Processing Systems 32, 2019	384	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	363	2021
Large-scale manifold learning A Talwalkar, S Kumar, H Rowley 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008	272	2008
Divide-and-Conquer Matrix Factorization LW Mackey, A Talwalkar, MI Jordan NIPS, 1134-1142, 2011	247	2011
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	245	2013

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