Kristjan Greenewald

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Alfred HeroJohn H Holland Distinguished University Professor, University of MichiganVerified email at eecs.umich.edu
Ziv GoldfeldCornell UniversityVerified email at cornell.edu
Mikhail YurochkinResearch Staff Member, IBM Research and MIT-IBM Watson AI LabVerified email at umich.edu
Soumya GhoshMIT-IBM Watson AI Lab, IBM ResearchVerified email at cs.brown.edu
Trong Nghia HoangAssistant Professor, Washington State UniversityVerified email at wsu.edu
Mayank AgarwalSenior Research Engineer, IBM ResearchVerified email at ibm.com
Yasaman KhazaeniWayfair, IBM, Boston UniversityVerified email at bu.edu
Neil ThompsonResearch Scientist, MIT Computer Science and A.I. Lab and Initiative on the Digital EconomyVerified email at mit.edu
Murat KocaogluAssistant Professor, ECE, Purdue UniversityVerified email at purdue.edu
Brian KingsburyDistinguished Research Staff Member and Manager, IBM T. J. Watson Research Center, Yorktown HeightsVerified email at us.ibm.com
Kevin R. MoonUtah State UniversityVerified email at usu.edu
Kumar SricharanPrincipal Data Scientist, Intuit Inc.Verified email at intuit.com
Predrag KlasnjaAssociate Professor, School of Information, University of MichiganVerified email at umich.edu
Susan MurphyHarvard UniversityVerified email at g.harvard.edu
Karthikeyan ShanmugamResearch Scientist, Google Research IndiaVerified email at google.com
Jonathan Niles-WeedNew York UniversityVerified email at cims.nyu.edu
Justin SolomonMITVerified email at mit.edu
Shuheng ZhouAssociate Professor, University of California, RiversideVerified email at ucr.edu
Sara MagliacaneUniversity of Amsterdam, MIT-IBM Watson AI LabVerified email at uva.nl
Peng LiaoHarvard UniversityVerified email at umich.edu

Kristjan Greenewald

Research Scientist, MIT-IBM Watson AI Lab, IBM Research

Verified email at ibm.com - Homepage

machine learning optimal transport causal inference information theory covariance estimation


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The computational limits of deep learning NC Thompson, K Greenewald, K Lee, GF Manso arXiv preprint arXiv:2007.05558, 2020	345	2020
Bayesian nonparametric federated learning of neural networks M Yurochkin, M Agarwal, S Ghosh, K Greenewald, N Hoang, Y Khazaeni International conference on machine learning, 7252-7261, 2019	311	2019
Estimating information flow in deep neural networks Z Goldfeld, E Berg, K Greenewald, I Melnyk, N Nguyen, B Kingsbury, ... arXiv preprint arXiv:1810.05728, 2018	132	2018
Personalized heartsteps: A reinforcement learning algorithm for optimizing physical activity P Liao, K Greenewald, P Klasnja, S Murphy Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous …, 2020	77	2020
Deep learning's diminishing returns: The cost of improvement is becoming unsustainable NC Thompson, K Greenewald, K Lee, GF Manso Ieee Spectrum 58 (10), 50-55, 2021	56	2021
Action centered contextual bandits K Greenewald, A Tewari, S Murphy, P Klasnja Advances in neural information processing systems 30, 2017	47	2017
Convergence of smoothed empirical measures with applications to entropy estimation Z Goldfeld, K Greenewald, J Niles-Weed, Y Polyanskiy IEEE Transactions on Information Theory 66 (7), 4368-4391, 2020	43	2020
Robust kronecker product PCA for spatio-temporal covariance estimation K Greenewald, AO Hero IEEE Transactions on Signal Processing 63 (23), 6368-6378, 2015	36	2015
Improving convergence of divergence functional ensemble estimators KR Moon, K Sricharan, K Greenewald, AO Hero 2016 IEEE International Symposium on Information Theory (ISIT), 1133-1137, 2016	33	2016
Tensor graphical lasso (TeraLasso) K Greenewald, S Zhou, A Hero III arXiv preprint arXiv:1705.03983, 2017	30	2017
Kronecker sum decompositions of space-time data K Greenewald, T Tsiligkaridis, AO Hero 2013 5th IEEE International Workshop on Computational Advances in Multi …, 2013	26	2013
Gaussian-smoothed optimal transport: Metric structure and statistical efficiency Z Goldfeld, K Greenewald International Conference on Artificial Intelligence and Statistics, 3327-3337, 2020	23	2020
Sample efficient active learning of causal trees K Greenewald, D Katz, K Shanmugam, S Magliacane, M Kocaoglu, ... Advances in Neural Information Processing Systems 32, 2019	23	2019
Statistical model aggregation via parameter matching M Yurochkin, M Agarwal, S Ghosh, K Greenewald, N Hoang Advances in neural information processing systems 32, 2019	21	2019
Nonparametric ensemble estimation of distributional functionals KR Moon, K Sricharan, K Greenewald, AO Hero arXiv preprint arXiv:1601.06884, 2016	21	2016
Ensemble estimation of information divergence KR Moon, K Sricharan, K Greenewald, AO Hero III Entropy 20 (8), 560, 2018	20	2018
Active structure learning of causal DAGs via directed clique trees C Squires, S Magliacane, K Greenewald, D Katz, M Kocaoglu, ... Advances in Neural Information Processing Systems 33, 21500-21511, 2020	17	2020
Robust SAR STAP via Kronecker decomposition K Greenewald, E Zelnio, AH Hero IEEE Transactions on Aerospace and Electronic Systems 52 (6), 2612-2625, 2016	17	2016
Asymptotic guarantees for generative modeling based on the smooth wasserstein distance Z Goldfeld, K Greenewald, K Kato Advances in neural information processing systems 33, 2527-2539, 2020	14	2020
Regularized block toeplitz covariance matrix estimation via kronecker product expansions K Greenewald, AO Hero 2014 IEEE Workshop on Statistical Signal Processing (SSP), 9-12, 2014	13	2014

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