Junhong Lin

发表

Generalization Properties and Implicit Regularization for Multiple Passes SGM

Lorenzo Rosasco, Junhong Lin, Raffaello Camoriano, 2016, ICML.

Kernel Conjugate Gradient Methods with Random Projections pdf

Volkan Cevher, Junhong Lin, V. Cevher, 2018, ArXiv.

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Volkan Cevher, Junhong Lin, V. Cevher, 2018, J. Mach. Learn. Res..

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods

Volkan Cevher, Junhong Lin, V. Cevher, 2018, ICML.

Convergence of projected Landweber iteration for matrix rank minimization

Junhong Lin, Song Li, 2014 .

Block sparse recovery via mixed l2/l1 minimization

Song Li, Junhong Lin, Song Li, 2013, Acta Mathematica Sinica, English Series.

Modified Fejér sequences and applications

Ding-Xuan Zhou, L. Rosasco, Junhong Lin, 2017, Computational optimization and applications.

Optimal rates for spectral algorithms with least-squares regression over Hilbert spaces

Volkan Cevher, Lorenzo Rosasco, Alessandro Rudi, 2018, Applied and Computational Harmonic Analysis.

Optimal Rates for Multi-pass Stochastic Gradient Methods

Lorenzo Rosasco, Junhong Lin, L. Rosasco, 2016, J. Mach. Learn. Res..

Modified Fejér sequences and applications

Lorenzo Rosasco, Silvia Villa, Ding-Xuan Zhou, 2015, Comput. Optim. Appl..

Low rank matrix recovery with adversarial sparse noise

Junhong Lin, Song Li, Hang Xu, 2021, Inverse Problems.

Iterative hard thresholding for compressed data separation

Wenchang Sun, Song Li, Junhong Lin, 2020, J. Complex..

Sparse Recovery with Coherent Tight Frame via Analysis Dantzig Selector and Analysis LASSO pdf

Song Li, Junhong Lin, Junhong Lin, 2013, ArXiv.

I T ] 8 S ep 2 01 3 Sparse Recovery with Coherent Tight Frames via Analysis Dantzig Selector and Analysis LASSO ∗

Junhong Lin, Song Li, 2021 .

Nonuniform support recovery from noisy random measurements by Orthogonal Matching Pursuit

Song Li, Junhong Lin, Junhong Lin, 2013, J. Approx. Theory.

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces

Volkan Cevher, Junhong Lin, V. Cevher, 2018, ICML.

Online Learning Algorithms Can Converge Comparably Fast as Batch Learning

Ding-Xuan Zhou, Junhong Lin, Ding-Xuan Zhou, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Chemical machine learning with kernels: The impact of loss functions

Volkan Cevher, Junhong Lin, Quang Van Nguyen, 2019, International Journal of Quantum Chemistry.

Learning theory of randomized Kaczmarz algorithm

Ding-Xuan Zhou, Junhong Lin, Ding-Xuan Zhou, 2015, J. Mach. Learn. Res..

Online pairwise learning algorithms with convex loss functions

Bo Zhang, Yunwen Lei, Ding-Xuan Zhou, 2017, Inf. Sci..

Iterative Regularization for Learning with Convex Loss Functions

Lorenzo Rosasco, Ding-Xuan Zhou, Junhong Lin, 2015, J. Mach. Learn. Res..

Rates of Convergence of Randomized Kaczmarz Algorithms in Hilbert Spaces

Ding-Xuan Zhou, Junhong Lin, Xin Guo, 2022, Applied and Computational Harmonic Analysis.

Optimal Learning for Multi-pass Stochastic Gradient Methods

Lorenzo Rosasco, Junhong Lin, L. Rosasco, 2016, NIPS.

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections

Volkan Cevher, Junhong Lin, V. Cevher, 2020, J. Mach. Learn. Res..

A Learning-Based Framework for Quantized Compressed Sensing

Volkan Cevher, Rabeeh Karimi Mahabadi, Junhong Lin, 2019, IEEE Signal Processing Letters.

Compressed Sensing with coherent tight frames via $l_q$-minimization for $0 pdf

Song Li, Junhong Lin, Junhong Lin, 2011, ArXiv.

New Bounds for Restricted Isometry Constants With Coherent Tight Frames

Song Li, Yi Shen, Junhong Lin, 2013, IEEE Transactions on Signal Processing.

Restricted q-Isometry Properties Adapted to Frames for Nonconvex lq-Analysis

Song Li, Junhong Lin, Junhong Lin, 2016, IEEE Trans. Inf. Theory.

Compressed Data Separation With Redundant Dictionaries

Song Li, Yi Shen, Junhong Lin, 2013, IEEE Transactions on Information Theory.

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral-Regularization Algorithms pdf

Volkan Cevher, Junhong Lin, V. Cevher, 2018, ArXiv.

Iterative Regularization for Learning with Convex Loss Functions

Ding-Xuan Zhou, L. Rosasco, Junhong Lin, 2015, J. Mach. Learn. Res..