| 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 | 669 | 2019 |
| The computational limits of deep learning NC Thompson, K Greenewald, K Lee, GF Manso arXiv preprint arXiv:2007.05558, 2020 | 630 | 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 | 185 | 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 | 171 | 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 | 135 | 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 | 81* | 2020 |
| Action centered contextual bandits K Greenewald, A Tewari, S Murphy, P Klasnja Advances in neural information processing systems 30, 2017 | 60 | 2017 |
| Tensor graphical lasso (TeraLasso) K Greenewald, S Zhou, A Hero III Journal of the Royal Statistical Society Series B: Statistical Methodology …, 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 | 44 | 2015 |
| 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 | 43 | 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 | 35 | 2020 |
| Statistical model aggregation via parameter matching M Yurochkin, M Agarwal, S Ghosh, K Greenewald, N Hoang Advances in neural information processing systems 32, 2019 | 34 | 2019 |
| Sliced mutual information: A scalable measure of statistical dependence Z Goldfeld, K Greenewald Advances in Neural Information Processing Systems 34, 17567-17578, 2021 | 33 | 2021 |
| 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 |
| Gaussian-smoothed optimal transport: Metric structure and statistical efficiency Z Goldfeld, K Greenewald International Conference on Artificial Intelligence and Statistics, 3327-3337, 2020 | 32 | 2020 |
| 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 |
| 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 | 26 | 2024 |
| 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 |
| Robust SAR STAP via Kronecker decomposition K Greenewald, E Zelnio, AH Hero IEEE Transactions on Aerospace and Electronic Systems 52 (6), 2612-2625, 2016 | 24 | 2016 |
Mikhail YurochkinResearch Staff Member, IBM Research and MIT-IBM Watson AI Lab在 umich.edu 的电子邮件经过验证
