Compressed Sensing with coherent tight frames via $l_q$-minimization for $0<q leq1$ - Song Li 002; Junhong Lin (CoRR, 2011)

Sparse Recovery with Coherent Tight Frame via Analysis Dantzig Selector and Analysis LASSO - Junhong Lin; Song Li 002 (CoRR, 2013)

Nonuniform support recovery from noisy random measurements by Orthogonal Matching Pursuit. - Junhong Lin; Song Li 002 (J. Approx. Theory, 2013)

Compressed Data Separation With Redundant Dictionaries. - Junhong Lin; Song Li 002; Yi Shen 009 (IEEE Trans. Inf. Theory, 2013)

New Bounds for Restricted Isometry Constants With Coherent Tight Frames. - Junhong Lin; Song Li 002; Yi Shen 009 (IEEE Trans. Signal Process., 2013)

Learning theory of randomized Kaczmarz algorithm. - Junhong Lin; Ding-Xuan Zhou (J. Mach. Learn. Res., 2015)

Generalization Properties and Implicit Regularization for Multiple Passes SGM. - Junhong Lin; Raffaello Camoriano; Lorenzo Rosasco (ICML, 2016)

Optimal Learning for Multi-pass Stochastic Gradient Methods. - Junhong Lin; Lorenzo Rosasco (NIPS, 2016)

Restricted $q$-Isometry Properties Adapted to Frames for Nonconvex $l_q$-Analysis. - Junhong Lin; Song Li 002 (CoRR, 2016)

Iterative Regularization for Learning with Convex Loss Functions. - Junhong Lin; Lorenzo Rosasco; Ding-Xuan Zhou (J. Mach. Learn. Res., 2016)

Restricted q-Isometry Properties Adapted to Frames for Nonconvex lq-Analysis. - Junhong Lin; Song Li 002 (IEEE Trans. Inf. Theory, 2016)

Generalization Properties of Doubly Online Learning Algorithms. - Junhong Lin; Lorenzo Rosasco (CoRR, 2017)

Optimal Rates for Learning with Nyström Stochastic Gradient Methods. - Junhong Lin; Lorenzo Rosasco (CoRR, 2017)

Online pairwise learning algorithms with convex loss functions. - Junhong Lin; Yunwen Lei; Bo Zhang; Ding-Xuan Zhou (Inf. Sci., 2017)

Optimal Rates for Multi-pass Stochastic Gradient Methods. - Junhong Lin; Lorenzo Rosasco (J. Mach. Learn. Res., 2017)

Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods. - Junhong Lin; Volkan Cevher (ICML, 2018)

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces. - Junhong Lin; Volkan Cevher (ICML, 2018)

Modified Fejér sequences and applications. - Junhong Lin; Lorenzo Rosasco; Silvia Villa; Ding-Xuan Zhou (Comput. Optim. Appl., 2018)

Optimal Rates for Spectral-regularized Algorithms with Least-Squares Regression over Hilbert Spaces. - Junhong Lin; Volkan Cevher (CoRR, 2018)

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral-Regularization Algorithms. - Junhong Lin; Volkan Cevher (CoRR, 2018)

Kernel Conjugate Gradient Methods with Random Projections. - Junhong Lin; Volkan Cevher (CoRR, 2018)

Generalization properties of doubly stochastic learning algorithms. - Junhong Lin; Lorenzo Rosasco (J. Complex., 2018)

Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples. - Junhong Lin; Bang Wang 001; Guang Yang; Mu Zhou (Sensors, 2018)

Online Learning Algorithms Can Converge Comparably Fast as Batch Learning. - Junhong Lin; Ding-Xuan Zhou (IEEE Trans. Neural Networks Learn. Syst., 2018)

A Learning-Based Framework for Quantized Compressed Sensing. - Rabeeh Karimi Mahabadi; Junhong Lin; Volkan Cevher (IEEE Signal Process. Lett., 2019)

Iterative hard thresholding for compressed data separation. - Song Li 002; Junhong Lin; Dekai Liu; Wenchang Sun (J. Complex., 2020)

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections. - Junhong Lin; Volkan Cevher (J. Mach. Learn. Res., 2020)

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms. - Junhong Lin; Volkan Cevher (J. Mach. Learn. Res., 2020)

Beetle Swarm Optimization Algorithm-Based Load Control with Electricity Storage. - Hengjing He; Shangli Zhou; Leping Zhang; Junhong Lin; Weile Chen; Di Wu (J. Control. Sci. Eng., 2020)

DesnowFormer: an effective transformer-based image desnowing network. - Ting Zhang; Nanfeng Jiang; Junhong Lin; Jielian Lin; Tiesong Zhao (VCIP, 2022)

Unsupervised detection of Small Hyperreflective Features in Ultrahigh Resolution Optical Coherence Tomography. - Marcel Reimann; Jungeun Won; Hiroyuki Takahashi; Antonio Yaghy; Yunchan Hwang; Stefan B. Ploner; Junhong Lin; Jessica Girgis; Kenneth Lam; Siyu Chen; Nadia K. Waheed; Andreas K. Maier; et al. (Bildverarbeitung für die Medizin, 2023)

LDRM: Degradation Rectify Model for Low-light Imaging via Color-Monochrome Cameras. - Junhong Lin; Shufan Pei; Bing Chen; Nanfeng Jiang; Wei Gao 003; Tiesong Zhao (ACM Multimedia, 2023)

High Probability Convergence of Adam Under Unbounded Gradients and Affine Variance Noise. - Yusu Hong; Junhong Lin (CoRR, 2023)

Lightweight Semi-supervised Network for Single Image Rain Removal. - Nanfeng Jiang; Jiawei Luo; Junhong Lin; Weiling Chen; Tiesong Zhao (Pattern Recognit., 2023)

Low-Light Image Enhancement via Stage-Transformer-Guided Network. - Nanfeng Jiang; Junhong Lin; Ting Zhang; Haifeng Zheng; Tiesong Zhao (IEEE Trans. Circuits Syst. Video Technol., 2023)

LMQFormer: A Laplace-Prior-Guided Mask Query Transformer for Lightweight Snow Removal. - Junhong Lin; Nanfeng Jiang; Zhentao Zhang; Weiling Chen; Tiesong Zhao (IEEE Trans. Circuits Syst. Video Technol., 2023)

UnifiedGT: Towards a Universal Framework of Transformers in Large-Scale Graph Learning. - Junhong Lin; Xiaojie Guo 002; Shuaicheng Zhang; Dawei Zhou 003; Yada Zhu; Julian Shun (IEEE Big Data, 2024)

FraudGT: A Simple, Effective, and Efficient Graph Transformer for Financial Fraud Detection. - Junhong Lin; Xiaojie Guo 002; Yada Zhu; Samuel Mitchell; Erik Altman; Julian Shun (ICAIF, 2024)

On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions. - Yusu Hong; Junhong Lin (NeurIPS, 2024)

Revisiting Convergence of AdaGrad with Relaxed Assumptions. - Yusu Hong; Junhong Lin (UAI, 2024)

Nonconvex Deterministic Matrix Completion by Projected Gradient Descent Methods. - Hang Xu; Song Li 002; Junhong Lin (CoRR, 2024)

PAC-Bayesian Adversarially Robust Generalization Bounds for Graph Neural Network. - Tan Sun; Junhong Lin (CoRR, 2024)

Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review. - Jing Su; Chufeng Jiang; Xin Jin; Yuxin Qiao; Tingsong Xiao; Hongda Ma; Rong Wei; Zhi Jing 003; Jiajun Xu; Junhong Lin (CoRR, 2024)

Beyond Night Visibility: Adaptive Multi-Scale Fusion of Infrared and Visible Images. - Shufan Pei; Junhong Lin; Wenxi Liu; Tiesong Zhao; Chia-Wen Lin (CoRR, 2024)

When Heterophily Meets Heterogeneity: New Graph Benchmarks and Effective Methods. - Junhong Lin; Xiaojie Guo 002; Shuaicheng Zhang; Dawei Zhou 003; Yada Zhu; Julian Shun (CoRR, 2024)

Robust Domain Generalization for Multi-modal Object Recognition. - Yuxin Qiao; Keqin Li; Junhong Lin; Rong Wei; Chufeng Jiang; Yang Luo; Haoyu Yang (CoRR, 2024)

Convergence of projected subgradient method with sparse or low-rank constraints. - Hang Xu; Song Li; Junhong Lin (Adv. Comput. Math., 2024)

Reasoning of Large Language Models over Knowledge Graphs with Super-Relations. - Song Wang 013; Junhong Lin; Xiaojie Guo 002; Julian Shun; Jundong Li; Yada Zhu (ICLR, 2025)

High probability bounds on AdaGrad for constrained weakly convex optimization. - Yusu Hong; Junhong Lin (J. Complex., 2025)

Low-Light Aerial Imaging With Color and Monochrome Cameras. - Pengwu Yuan; Liqun Lin; Junhong Lin; Yipeng Liao; Tiesong Zhao (IEEE Trans. Geosci. Remote. Sens., 2025)

ConstellationNet: Reinventing Spatial Clustering through GNNs. - Aidan Gao; Junhong Lin (CoRR, 2025)

LENSLLM: Unveiling Fine-Tuning Dynamics for LLM Selection. - Xinyue Zeng; Haohui Wang; Junhong Lin; Jun Wu 019; Tyler Cody; Dawei Zhou 003 (CoRR, 2025)

Plan and Budget: Effective and Efficient Test-Time Scaling on Large Language Model Reasoning. - Junhong Lin; Xinyue Zeng; Jie Zhu; Song Wang 013; Julian Shun; Jun Wu 019; Dawei Zhou 003 (CoRR, 2025)