Student-t Process Regression with Dependent Student-t Noise. - Qingtao Tang; Yisen Wang 001; Shu-Tao Xia (ECAI, 2016)

Bernoulli Random Forests: Closing the Gap between Theoretical Consistency and Empirical Soundness. - Yisen Wang 001; Qingtao Tang; Shu-Tao Xia; Jia Wu 001; Xingquan Zhu 001 (IJCAI, 2016)

Foveated nonlocal dual denoising. - Tao Dai 001; Ke Gu 001; Qingtao Tang; Kwok-Wai Hung; Yongbing Zhang 002; Weizhi Lu; Shu-Tao Xia (ICIP, 2017)

Blind quality assessment of multiply-distorted images based on structural degradation. - Tao Dai 001; Ke Gu 001; Zhiya Xu; Qingtao Tang; Haoyi Liang; Yongbing Zhang 002; Shu-Tao Xia (ICIP, 2017)

Robust Survey Aggregation with Student-t Distribution and Sparse Representation. - Qingtao Tang; Tao Dai 001; Li Niu 002; Yisen Wang 001; Shu-Tao Xia; Jianfei Cai 001 (IJCAI, 2017)

Student-t Process Regression with Student-t Likelihood. - Qingtao Tang; Li Niu 002; Yisen Wang 001; Tao Dai 001; Wangpeng An; Jianfei Cai 001; Shu-Tao Xia (IJCAI, 2017)

A generic denoising framework via guided principal component analysis. - Tao Dai 001; Zhiya Xu; Haoyi Liang; Ke Gu 001; Qingtao Tang; Yisen Wang 001; Weizhi Lu; Shu-Tao Xia (J. Vis. Commun. Image Represent., 2017)

Learning From Noisy Web Data With Category-Level Supervision. - Li Niu 002; Qingtao Tang; Ashok Veeraraghavan; Ashutosh Sabharwal (CVPR, 2018)

Sure-Based Dual Domain Image Denoising. - Zhiya Xu; Tao Dai 001; Li Niu 002; Jiawei Li 006; Qingtao Tang; Shu-Tao Xia (ICASSP, 2018)

Self -Paced Mixture of T Distribution Model. - Yang Zhang 016; Qingtao Tang; Li Niu 002; Tao Dai 001; Xi Xiao; Shu-Tao Xia (ICASSP, 2018)

Cyclic Annealing Training Convolutional Neural Networks for Image Classification with Noisy Labels. - Jiawei Li 006; Tao Dai 001; Qingtao Tang; Yeli Xing; Shu-Tao Xia (ICIP, 2018)

Portrait-Aware Artistic Style Transfer. - Yeli Xing; Jiawei Li 006; Tao Dai 001; Qingtao Tang; Li Niu 002; Shu-Tao Xia (ICIP, 2018)

A Novel Consistent Random Forest Framework: Bernoulli Random Forests. - Yisen Wang 001; Shu-Tao Xia; Qingtao Tang; Jia Wu 001; Xingquan Zhu 001 (IEEE Trans. Neural Networks Learn. Syst., 2018)

Multinomial Random Forests: Fill the Gap between Theoretical Consistency and Empirical Soundness. - Yiming Li 004; Jiawang Bai; Qingtao Tang; Yong Jiang 001; Chun Li; Shutao Xia (CoRR, 2019)

t-k-means: A k-means Variant with Robustness and Stability. - Yang Zhang 016; Qingtao Tang; Yiming Li 004; Weipéng Huáng; Shutao Xia (CoRR, 2019)

JSRT: James-Stein Regression Tree. - Xingchun Xiang; Qingtao Tang; Huaixuan Zhang; Tao Dai 001; Jiawei Li 006; Shu-Tao Xia (CoRR, 2020)

t-k-means: A ROBUST AND STABLE k-means VARIANT. - Yiming Li 004; Yang Zhang 016; Qingtao Tang; Weipéng Huáng; Yong Jiang 001; Shu-Tao Xia (ICASSP, 2021)

Deep Position-wise Interaction Network for CTR Prediction. - Jianqiang Huang; Ke Hu; Qingtao Tang; Mingjian Chen; Yi Qi; Jia Cheng; Jun Lei (SIGIR, 2021)

Hybrid CNN Based Attention with Category Prior for User Image Behavior Modeling. - Xin Chen; Qingtao Tang; Ke Hu; Yue Xu; Shihang Qiu; Jia Cheng; Jun Lei (SIGIR, 2022)

An Autonomous Trajectory Adjustment Method for Robot Demonstration Learning Trajectory Based on Visual Assistance. - Kaiyin Hu; Hao Chu; Xibin Xiao; Qingtao Tang; Fei Wang 048 (ROBIO, 2024)