Kristjan Greenewald

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Mikhail YurochkinResearch Manager, Statistical Large Language Modeling, IBM Research and MIT-IBM Watson AI LabVerified email at umich.edu
Ziv GoldfeldCornell UniversityVerified email at cornell.edu
Alfred HeroJohn H Holland Distinguished University Professor, University of MichiganVerified email at eecs.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
Neil ThompsonResearch Scientist, MIT Computer Science and A.I. Lab and Initiative on the Digital EconomyVerified email at mit.edu
Keeheon LeeYonsei UniversityVerified email at yonsei.ac.kr
Gabriel MansoMIT EECSVerified email at mit.edu
Mayank AgarwalSenior Research Engineer, IBM ResearchVerified email at ibm.com
Yasaman KhazaeniCapitalOne, Wayfair, IBM, Boston UniversityVerified email at bu.edu
Igor MelnykCapital OneVerified email at capitalone.com
Murat KocaogluAssistant Professor, School of ECE, Purdue UniversityVerified email at purdue.edu
Justin SolomonMITVerified email at mit.edu
Karthikeyan ShanmugamResearch Scientist, Google Research IndiaVerified email at google.com
Brian KingsburyDistinguished Research Staff Member and Manager, IBM T. J. Watson Research Center, Yorktown HeightsVerified email at us.ibm.com
Predrag KlasnjaAssociate Professor, School of Information, University of MichiganVerified email at umich.edu
Susan MurphyHarvard UniversityVerified email at g.harvard.edu
Ewout van den BergIBM T.J. Watson Research CenterVerified email at us.ibm.com
Shuheng ZhouProfessor, University of California, RiversideVerified email at ucr.edu
Nam H. NguyenCapital OneVerified email at capitalone.com

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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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	758	2019
The computational limits of deep learning NC Thompson, K Greenewald, K Lee, GF Manso arXiv preprint arXiv:2007.05558 10, 2020	720	2020
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	189	2020
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	168	2018
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	146	2021
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	85*	2020
Action centered contextual bandits K Greenewald, A Tewari, S Murphy, P Klasnja Advances in neural information processing systems 30, 2017	61	2017
Tensor graphical lasso (TeraLasso) K Greenewald, S Zhou, A Hero III Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2019	50	2019
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	45	2019
Robust kronecker product PCA for spatio-temporal covariance estimation K Greenewald, AO Hero IEEE Transactions on Signal Processing 63 (23), 6368-6378, 2015	45	2015
Identifiability guarantees for causal disentanglement from soft interventions J Zhang, K Greenewald, C Squires, A Srivastava, K Shanmugam, C Uhler Advances in Neural Information Processing Systems 36, 2024	39	2024
Sliced mutual information: A scalable measure of statistical dependence Z Goldfeld, K Greenewald Advances in Neural Information Processing Systems 34, 17567-17578, 2021	39	2021
Statistical model aggregation via parameter matching M Yurochkin, M Agarwal, S Ghosh, K Greenewald, N Hoang Advances in neural information processing systems 32, 2019	39	2019
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	37	2020
Gaussian-smoothed optimal transport: Metric structure and statistical efficiency Z Goldfeld, K Greenewald International Conference on Artificial Intelligence and Statistics, 3327-3337, 2020	36	2020
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
Kronecker sum decompositions of space-time data K Greenewald, T Tsiligkaridis, AO Hero 2013 5th IEEE International Workshop on Computational Advances in Multi …, 2013	29	2013
Estimating information flow in neural networks Z Goldfeld, E van den Berg, K Greenewald, I Melnyk, N Nguyen, ... arXiv preprint arXiv:1810.05728, 2018	27	2018
Ensemble estimation of information divergence KR Moon, K Sricharan, K Greenewald, AO Hero III Entropy 20 (8), 560, 2018	26	2018
Measuring generalization with optimal transport CY Chuang, Y Mroueh, K Greenewald, A Torralba, S Jegelka Advances in neural information processing systems 34, 8294-8306, 2021	25	2021

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