| Learning theory approach to minimum error entropy criterion T Hu, J Fan, Q Wu, DX Zhou arXiv preprint arXiv:1208.0848, 2012 | 90 | 2012 |
| A statistical learning approach to modal regression Y Feng, J Fan, JAK Suykens Journal of Machine Learning Research 21 (2), 1−35, 2020 | 86 | 2020 |
| Regularization schemes for minimum error entropy principle T Hu, J Fan, Q Wu, DX Zhou Analysis and Applications 13 (04), 437-455, 2015 | 81 | 2015 |
| Consistency analysis of an empirical minimum error entropy algorithm J Fan, T Hu, Q Wu, DX Zhou Applied and Computational Harmonic Analysis 41 (1), 164-189, 2016 | 55 | 2016 |
| Optimal learning with Gaussians and correntropy loss F Lv, J Fan Analysis and Applications 19 (01), 107-124, 2021 | 29 | 2021 |
| Learning rates for regularized least squares ranking algorithm Y Zhao, J Fan, L Shi Analysis and Applications 15 (06), 815-836, 2017 | 26 | 2017 |
| Parameterized BLOSUM matrices for protein alignment D Song, J Chen, G Chen, N Li, J Li, J Fan, D Bu, SC Li IEEE/ACM Transactions on Computational Biology and Bioinformatics 12 (3 …, 2014 | 23 | 2014 |
| Structure-leveraged methods in breast cancer risk prediction J Fan, Y Wu, M Yuan, D Page, J Liu, IM Ong, P Peissig, E Burnside Journal of Machine Learning Research 17 (85), 1-15, 2016 | 21 | 2016 |
| Online gradient descent algorithms for functional data learning X Chen, B Tang, J Fan, X Guo Journal of Complexity 70, 101635, 2022 | 20 | 2022 |
| Breast cancer risk prediction using electronic health records Y Wu, ES Burnside, J Cox, J Fan, M Yuan, J Yin, P Peissig, A Cobian, ... 2017 IEEE International Conference on Healthcare Informatics (ICHI), 224-228, 2017 | 20 | 2017 |
| Comparing mammography abnormality features to genetic variants in the prediction of breast cancer in women recommended for breast biopsy ES Burnside, J Liu, Y Wu, AA Onitilo, CA McCarty, CD Page, PL Peissig, ... Academic radiology 23 (1), 62-69, 2016 | 14 | 2016 |
| An RKHS approach to estimate individualized treatment rules based on functional predictors. J Fan, F Lv, L Shi Math. Found. Comput. 2 (2), 169-181, 2019 | 10 | 2019 |
| Sparsity and error analysis of empirical feature-based regularization schemes X Guo, J Fan, DX Zhou Journal of Machine Learning Research 17 (89), 1-34, 2016 | 10 | 2016 |
| Approximation of nonlinear functionals using deep ReLU networks L Song, J Fan, DR Chen, DX Zhou Journal of Fourier Analysis and Applications 29 (4), 50, 2023 | 9 | 2023 |
| Quantifying predictive capability of electronic health records for the most harmful breast cancer Y Wu, J Fan, P Peissig, R Berg, AP Tafti, J Yin, M Yuan, D Page, J Cox, ... Medical Imaging 2018: Image Perception, Observer Performance, and Technology …, 2018 | 9 | 2018 |
| Utility of genetic testing in addition to mammography for determining risk of breast cancer depends on patient age SI Feld, J Fan, M Yuan, Y Wu, KM Woo, R Alexandridis, ES Burnside AMIA Summits on Translational Science Proceedings 2018, 81, 2018 | 9 | 2018 |
| Approximation of smooth functionals using deep ReLU networks L Song, Y Liu, J Fan, DX Zhou Neural Networks 166, 424-436, 2023 | 7 | 2023 |
| Convergence analysis of distributed multi-penalty regularized pairwise learning T Hu, J Fan, DH Xiang Analysis and Applications 18 (01), 109-127, 2020 | 7 | 2020 |
| Discriminatory power of common genetic variants in personalized breast cancer diagnosis Y Wu, CK Abbey, J Liu, I Ong, P Peissig, AA Onitilo, J Fan, M Yuan, ... Medical Imaging 2016: Image Perception, Observer Performance, and Technology …, 2016 | 6 | 2016 |
| Comparison theorems on large-margin learning A Benabid, J Fan, DH Xiang International Journal of Wavelets, Multiresolution and Information …, 2021 | 4 | 2021 |
