Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods. - Junhong Lin 003; Volkan Cevher (ICML, 2018)

Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces. - Junhong Lin 003; Volkan Cevher (ICML, 2018)

Optimal Rates for Spectral-regularized Algorithms with Least-Squares Regression over Hilbert Spaces. - Junhong Lin 003; Volkan Cevher (CoRR, 2018)

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral-Regularization Algorithms. - Junhong Lin 003; Volkan Cevher (CoRR, 2018)

Kernel Conjugate Gradient Methods with Random Projections. - Junhong Lin 003; Volkan Cevher (CoRR, 2018)

A Learning-Based Framework for Quantized Compressed Sensing. - Rabeeh Karimi Mahabadi; Junhong Lin 003; Volkan Cevher (IEEE Signal Process. Lett., 2019)

Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections. - Junhong Lin 003; Volkan Cevher (J. Mach. Learn. Res., 2020)

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms. - Junhong Lin 003; Volkan Cevher (J. Mach. Learn. Res., 2020)

PAC-Bayesian Adversarially Robust Generalization Bounds for Graph Neural Network. - Tan Sun; Junhong Lin 003 (CoRR, 2024)

Convergence Theory of Sharpness-Aware Minimization With Interpolating Neural Networks. - Tan Sun; Yusu Hong; Junhong Lin 003 (IEEE Trans. Signal Process., 2026)