Recursive Support Vector Machines for Dimensionality Reduction. - Qing Tao; Dejun Chu; Jue Wang (IEEE Trans. Neural Networks, 2008)

Stochastic Coordinate Descent Methods for Regularized Smooth and Nonsmooth Losses. - Qing Tao; Kang Kong; Dejun Chu; Gao-wei Wu (ECML/PKDD (1), 2012)

A faster cutting plane algorithm with accelerated line search for linear SVM. - Dejun Chu; Changshui Zhang; Qing Tao (Pattern Recognit., 2017)

Optimizing Top-k Multiclass SVM via Semismooth Newton Algorithm. - Dejun Chu; Rui Lu; Jin Li 020; Xintong Yu; Changshui Zhang; Qing Tao (IEEE Trans. Neural Networks Learn. Syst., 2018)

Improving Sparsity and Scalability in Regularized Nonconvex Truncated-Loss Learning Problems. - Qing Tao; Gao-wei Wu; Dejun Chu (IEEE Trans. Neural Networks Learn. Syst., 2018)

Semismooth Newton Algorithm for Efficient Projections onto ℓ1, ∞-norm Ball. - Dejun Chu; Changshui Zhang; Shiliang Sun; Qing Tao (ICML, 2020)

Structured BFGS Method for Optimal Doubly Stochastic Matrix Approximation. - Dejun Chu; Changshui Zhang; Shiliang Sun; Qing Tao (AAAI, 2023)