| Optimal learning for multi-pass stochastic gradient methods J Lin, L Rosasco Advances in Neural Information Processing Systems, 4556-4564, 2016 | 67* | 2016 |
| Generalization properties and implicit regularization for multiple passes sgm J Lin, R Camoriano, L Rosasco International Conference on Machine Learning, 2340-2348, 2016 | 48 | 2016 |
| New bounds for restricted isometry constants with coherent tight frames J Lin, S Li, Y Shen IEEE Transactions on Signal Processing 61 (3), 611-621, 2012 | 41 | 2012 |
| Optimal rates for spectral algorithms with least-squares regression over hilbert spaces J Lin, A Rudi, L Rosasco, V Cevher arXiv preprint arXiv:1801.06720, 2018 | 39 | 2018 |
| Optimal rates for spectral algorithms with least-squares regression over hilbert spaces J Lin, A Rudi, L Rosasco, V Cevher Applied and Computational Harmonic Analysis 48 (3), 868-890, 2020 | 35 | 2020 |
| Optimal rates for multi-pass stochastic gradient methods J Lin, L Rosasco The Journal of Machine Learning Research 18 (1), 3375-3421, 2017 | 35 | 2017 |
| Sparse recovery with coherent tight frames via analysis Dantzig selector and analysis LASSO J Lin, S Li Applied and Computational Harmonic Analysis 37 (1), 126-139, 2014 | 30 | 2014 |
| Iterative regularization for learning with convex loss functions J Lin, L Rosasco, DX Zhou The Journal of Machine Learning Research 17 (1), 2718-2755, 2016 | 27 | 2016 |
| Block sparse recovery via mixed l 2/l 1 minimization JH Lin, S Li Acta Mathematica Sinica, English Series 29 (7), 1401-1412, 2013 | 27 | 2013 |
| Compressed sensing with coherent tight frame via lq minimization S Li, J Lin Inverse Probl Imaging 8 (3), 761-777, 2014 | 25* | 2014 |
| Learning theory of randomized Kaczmarz algorithm J Lin, DX Zhou The Journal of Machine Learning Research 16 (1), 3341-3365, 2015 | 23 | 2015 |
| Compressed data separation with redundant dictionaries J Lin, S Li, Y Shen IEEE transactions on information theory 59 (7), 4309-4315, 2013 | 17 | 2013 |
| Online learning algorithms can converge comparably fast as batch learning J Lin, DX Zhou IEEE transactions on neural networks and learning systems 29 (6), 2367-2378, 2017 | 15 | 2017 |
| Optimal convergence for distributed learning with stochastic gradient methods and spectral algorithms J Lin, V Cevher arXiv preprint arXiv:1801.07226, 2018 | 14 | 2018 |
| Restricted-Isometry Properties Adapted to Frames for Nonconvex-Analysis J Lin, S Li IEEE Transactions on Information Theory 62 (8), 4733-4747, 2016 | 13 | 2016 |
| Convergence of projected Landweber iteration for matrix rank minimization J Lin, S Li Applied and Computational Harmonic Analysis 36 (2), 316-325, 2014 | 13 | 2014 |
| Online pairwise learning algorithms with convex loss functions J Lin, Y Lei, B Zhang, DX Zhou Information Sciences 406, 57-70, 2017 | 10 | 2017 |
| Nonuniform support recovery from noisy random measurements by orthogonal matching pursuit J Lin, S Li Journal of Approximation Theory 165 (1), 20-40, 2013 | 10 | 2013 |
| Optimal distributed learning with multi-pass stochastic gradient methods J Lin, V Cevher Proceedings of the 35th International Conference on Machine Learning, 2018 | 8 | 2018 |
| Modified Fejér sequences and applications J Lin, L Rosasco, S Villa, DX Zhou Computational Optimization and Applications 71 (1), 95-113, 2018 | 7 | 2018 |
