Junhong Lin
Junhong Lin
Center for Data Science, Zhejiang University
Verifierad e-postadress på epfl.ch - Startsida
TitelCiteras avÅr
Optimal rates for multi-pass stochastic gradient methods
J Lin, L Rosasco
Journal of Machine Learning Research 18 (97), 1-47, 2017
462017
Optimal learning for multi-pass stochastic gradient methods
J Lin, L Rosasco
Advances in Neural Information Processing Systems, 4556-4564, 2016
46*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
362012
Generalization properties and implicit regularization for multiple passes SGM
J Lin, R Camoriano, L Rosasco
International Conference on Machine Learning, 2340-2348, 2016
272016
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
252014
Compressed sensing with coherent tight frame via lq minimization
S Li, J Lin
Inverse Probl Imaging 8 (3), 761-777, 2014
25*2014
Block sparse recovery via mixed l 2/l 1 minimization
JH Lin, S Li
Acta Mathematica Sinica, English Series 29 (7), 1401-1412, 2013
232013
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
222016
Learning theory of randomized Kaczmarz algorithm
J Lin, DX Zhou
The Journal of Machine Learning Research 16 (1), 3341-3365, 2015
222015
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, 2018
21*2018
Compressed data separation with redundant dictionaries
J Lin, S Li, Y Shen
IEEE transactions on information theory 59 (7), 4309-4315, 2013
132013
Convergence of projected Landweber iteration for matrix rank minimization
J Lin, S Li
Applied and Computational Harmonic Analysis 36 (2), 316-325, 2014
122014
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
112017
Restricted-Isometry Properties Adapted to Frames for Nonconvex-Analysis
J Lin, S Li
IEEE Transactions on Information Theory 62 (8), 4733-4747, 2016
102016
Nonuniform support recovery from noisy random measurements by orthogonal matching pursuit
J Lin, S Li
Journal of Approximation Theory 165 (1), 20-40, 2013
92013
Optimal convergence for distributed learning with stochastic gradient methods and spectral algorithms
J Lin, V Cevher
arXiv preprint arXiv:1801.07226, 2018
82018
Online pairwise learning algorithms with convex loss functions
J Lin, Y Lei, B Zhang, DX Zhou
Information Sciences 406, 57-70, 2017
82017
Modified Fejér sequences and applications
J Lin, L Rosasco, S Villa, DX Zhou
Computational Optimization and Applications 71 (1), 95-113, 2018
72018
Optimal Rates for Learning with Nystr\" om Stochastic Gradient Methods
J Lin, L Rosasco
arXiv preprint arXiv:1710.07797, 2017
52017
Optimal distributed learning with multi-pass stochastic gradient methods
J Lin, V Cevher
Proceedings of the 35th International Conference on Machine Learning, 2018
42018
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Artiklar 1–20