| A more powerful two-sample test in high dimensions using random projection M Lopes, L Jacob, MJ Wainwright Advances in Neural Information Processing Systems 24, 2011 | 153 | 2011 |
| Estimating unknown sparsity in compressed sensing M Lopes International Conference on Machine Learning, 217-225, 2013 | 134 | 2013 |
| Unknown sparsity in compressed sensing: Denoising and inference ME Lopes IEEE Transactions on Information Theory 62 (9), 5145-5166, 2016 | 71 | 2016 |
| Central limit theorem and bootstrap approximation in high dimensions: Near 1/√n rates via implicit smoothing ME Lopes The Annals of Statistics 50 (5), 2492-2513, 2022 | 35 | 2022 |
| Bootstrapping Max Statistics in High Dimensions: Near-Parametric Rates Under Weak Variance Decay and Application to Functional and Multinomial Data ME Lopes, Z Lin, HG Mueller The Annals of Statistics, 2020 | 28 | 2020 |
| Estimating the algorithmic variance of randomized ensembles via the bootstrap ME Lopes The Annals of Statistics, 2019 | 28 | 2019 |
| Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap M Lopes, M., and Wang, S., and Mahoney International Conference on Machine Learning, 2018 | 25 | 2018 |
| Randomized numerical linear algebra: A perspective on the field with an eye to software R Murray, J Demmel, MW Mahoney, NB Erichson, M Melnichenko, ... arXiv preprint arXiv:2302.11474, 2023 | 22 | 2023 |
| A residual bootstrap for high-dimensional regression with near low-rank designs M Lopes Advances in Neural Information Processing Systems 27, 2014 | 18 | 2014 |
| Bootstrapping the operator norm in high dimensions: Error estimation for covariance matrices and sketching ME Lopes, NB Erichson, MW Mahoney Bernoulli 29 (1), 428-450, 2023 | 15 | 2023 |
| A sharp lower-tail bound for Gaussian maxima with application to bootstrap methods in high dimensions ME Lopes, J Yao Electronic Journal of Statistics 16 (1), 58-83, 2022 | 15* | 2022 |
| Estimating a sharp convergence bound for randomized ensembles ME Lopes Journal of Statistical Planning and Inference 204, 35-44, 2020 | 15* | 2020 |
| Bootstrapping spectral statistics in high dimensions M Lopes, A Blandino, A Aue Biometrika 106 (4), 781-801, 2019 | 15 | 2019 |
| Error estimation for sketched SVD via the bootstrap M Lopes, NB Erichson, M Mahoney International Conference on Machine Learning, 6382-6392, 2020 | 14 | 2020 |
| A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication ME Lopes, S Wang, MW Mahoney Journal of Machine Learning Research 20 (39), 1-40, 2019 | 14 | 2019 |
| Randomized algorithms for scientific computing (RASC) A Buluc, TG Kolda, SM Wild, M Anitescu, A Degennaro, J Jakeman, ... arXiv preprint arXiv:2104.11079, 2021 | 12 | 2021 |
| High-dimensional MANOVA via bootstrapping and its application to functional and sparse count data Z Lin, ME Lopes, HG Müller Journal of the American Statistical Association 118 (541), 177-191, 2023 | 11 | 2023 |
| Measuring the algorithmic convergence of randomized ensembles: The regression setting ME Lopes, S Wu, TCM Lee SIAM Journal on Mathematics of Data Science 2 (4), 921-943, 2020 | 8 | 2020 |
| Rates of bootstrap approximation for eigenvalues in high-dimensional PCA J Yao, ME Lopes to appear: Statistica Sinica, arXiv preprint arXiv:2104.07328, 2021 | 7 | 2021 |
| Estimating the error of randomized newton methods: A bootstrap approach JXT Chen, M Lopes International Conference on Machine Learning, 1649-1659, 2020 | 5 | 2020 |
