| Approximation by Combinations of ReLU and Squared ReLU Ridge Functions WithandControls JM Klusowski, AR Barron IEEE Transactions on Information Theory 64 (12), 7649-7656, 2018 | 99 | 2018 |
| Approximation and estimation for high-dimensional deep learning networks AR Barron, JM Klusowski arXiv preprint arXiv:1809.03090, 2018 | 56 | 2018 |
| Risk bounds for high-dimensional ridge function combinations including neural networks JM Klusowski, AR Barron arXiv preprint arXiv:1607.01434, 2016 | 56 | 2016 |
| Algorithmic analysis and statistical estimation of slope via approximate message passing Z Bu, J Klusowski, C Rush, W Su Advances in Neural Information Processing Systems 32, 2019 | 44 | 2019 |
| Estimating the coefficients of a mixture of two linear regressions by expectation maximization JM Klusowski, D Yang, WD Brinda IEEE Transactions on Information Theory 65 (6), 3515-3524, 2019 | 36 | 2019 |
| Complexity, statistical risk, and metric entropy of deep nets using total path variation AR Barron, JM Klusowski arXiv preprint arXiv:1902.00800, 2019 | 26 | 2019 |
| Counting motifs with graph sampling JM Klusowski, Y Wu Conference On Learning Theory, 1966-2011, 2018 | 24 | 2018 |
| Statistical guarantees for estimating the centers of a two-component Gaussian mixture by EM JM Klusowski, WD Brinda arXiv preprint arXiv:1608.02280, 2016 | 23 | 2016 |
| Sharp analysis of a simple model for random forests J Klusowski International Conference on Artificial Intelligence and Statistics, 757-765, 2021 | 20 | 2021 |
| Complete analysis of a random forest model JM Klusowski arXiv preprint arXiv:1805.02587, 2018 | 20 | 2018 |
| Estimating the number of connected components in a graph via subgraph sampling JM Klusowski, Y Wu | 16 | 2020 |
| Uniform approximation by neural networks activated by first and second order ridge splines JM Klusowski, AR Barron arXiv preprint arXiv:1607.07819, 2016 | 16 | 2016 |
| Sparse learning with CART J Klusowski Advances in Neural Information Processing Systems 33, 11612-11622, 2020 | 15 | 2020 |
| Minimax lower bounds for ridge combinations including neural nets JM Klusowski, AR Barron 2017 IEEE International Symposium on Information Theory (ISIT), 1376-1380, 2017 | 14 | 2017 |
| Characterizing the SLOPE trade-off: A variational perspective and the Donoho–Tanner limit Z Bu, JM Klusowski, C Rush, WJ Su The Annals of Statistics 51 (1), 33-61, 2023 | 8 | 2023 |
| Estimating the coefficients of a mixture of two linear regressions by expectation maximization JM Klusowski, D Yang, WD Brinda arXiv preprint arXiv:1704.08231, 2017 | 8 | 2017 |
| Estimation of convex supports from noisy measurements VE Brunel, JM Klusowski, D Yang | 6 | 2021 |
| Universal consistency of decision trees in high dimensions JM Klusowski arXiv preprint arXiv:2104.13881, 2021 | 6 | 2021 |
| Uniform approximation by neural networks activated by first and second order ridge splines AR Barron, JM Klusowski arXiv preprint arXiv:1607.07819, 2016 | 6 | 2016 |
| Analyzing cart JM Klusowski arXiv preprint arXiv:1906.10086, 2019 | 5 | 2019 |
Andrew R BarronProfessor of Statistics, Yale UniversityVerified email at yale.edu
