Publications

[MICCAI-23] Li, C., Bagher-Ebadian, H., Goddla, V., Chetty, I. J., Zhu, D. (2023) FocalUNETR: A Focal Transformer for Boundary-aware Prostate Segmentation using CT Images. Accepted by the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI-23). Acceptance rate: 740/2250 = 32%.

[ECML-22] Li, C., Dong, Z, Fisher, N, and Zhu, D. (2022) Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation. To appear in the Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Acceptance rate: 242/932 = 26%.

[AdvML@ICML-22] Li, X., Qiang, Y. Li, C., Liu, S. and Zhu, D. (2022) Saliency guided adversarial training for tackling generalization gap with applications to medical imaging classification system. In the proceedings of new frontiers in adversarial machine learning (AdvML) workshop at ICML, 2022. 

[IJCAI-22] Qiang, Y, Li, C, Brocanelli, M, Zhu, D. (2022) Counterfactual Interpolation Augmentation (CIA): A Unified Approach to Enhance Fairness and Explainability of DNN. Proceedings of 31st International Joint Conference on Artificial Intelligence, Messe Wien, Vienna, Austria. Acceptance rate: 681/4,535 = 15%.

[IJCNN-22] Qiang, Y, Supriya TS. Kumar, Brocanelli, M, Zhu, D. (2022) Tiny RNN Model with Certified Robustness for Text Classification. Proceedings of International Joint Conference on Neural Networks (Oral Presentation). 

Pan, D, Li, X and Zhu, D. (2021) Explaining Deep Neural Network Models with Adversarial Gradient Integration. In the proceedings of 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Montreal, Canada.

Wang, L. and Zhu, D. (2021). Tackling multiple ordinal regression problems: sparse and deep multi-task learning approaches. Data Mining and Knowledge Discovery (DMKD), 23 March 2021.

Li, X., Pan, D. and Zhu, D. (2021) Defending against adversarial attacks on medical imaging AI system, classification or detection? In the proceedings of IEEE International Symposium on Biomedical Imaging (ISBI-21), virtual conference.

Li, X, Li, X, Pan,D and Zhu, D. (2021) Improving adversarial robustness via probabilistically compact loss with logit constraints. In the proceedings of Thirty-Five AAAI Conference on Artificial Intelligence (AAAI-21), virtual conference. Code

Pan, D, Li, X, Li, X and Zhu, D (2020) Explainable recommendation via interpretable feature mapping and evaluating explainability. to appear in the proceedings of 29th International Joint Conference on Artificial Intelligence (IJCAI-20), Yokohama, Japan.  

Li, X and Zhu, D (2020) COVID-MobileXpert: On-Device COVID-19 Screening using Snapshots of Chest X-Ray. arXiv:3119280 [cs.CV] 

Li, X, Pan,D,Li, X and Zhu, D (2020) Regularize SGD training via aligning min-batches.arXiv:2002.09917 [cs.LG].

Qiang, Y, Li, X and Zhu, D (2020) Toward tag-free aspect based sentiment analysis: a multiple attention network approach. to appear in the proceedings of  International Joint Conference on Neural Networks (IJCNN-20), Glasgow, Scotland, UK.

Li, X, Li, X, Pan,D and Zhu, D (2020) On the learning behavior of logistic and softmax losses for deep neural networks. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), New York.

Li, X, Zhu, D and Levy, P (2020) Predicting clinical outcomes with patient stratification via deep mixture neural networks. American Medical Informatics Association (AMIA-20) Summit on Clinical Research Informatics, San Francisco, accepted.

Li, X. and Zhu, D. (2020). Robust detection of adversarial attacks on medical images. IEEE International Symposium on Biomedical Imaging (ISBI-20), Iowa City.

Li, X., Hect, J., Thompson, J. and Zhu, D. (2020). Interpreting age effects of human fetal brain from spontaneous fMRI using deep 3D convolutional neural networks. IEEE International Symposium on Biomedical Imaging (ISBI-20), Iowa City.

Wang, L., & Zhu, D. (2019). Tackling Multiple Ordinal Regression Problems: Sparse and Deep Multi-Task Learning Approaches. arXiv preprint arXiv:1907.12508.

Li, X., & Zhu, D. (2019). CRCEN: A Generalized Cost-sensitive Neural Network Approach for Imbalanced Classification. arXiv preprint arXiv:1906.04026.

Li, X., Hect, J., Thomason, M., & Zhu, D. (2019). Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks. arXiv preprint arXiv:1906.03691.

Li, X., Cao, R., & Zhu, D. (2019). Vispi: Automatic Visual Perception and Interpretation of Chest X-rays. arXiv preprint arXiv:1906.05190.

Nezhad, MZ, Sadati, N, Yang, K and Zhu, D. A deep active survival analysis approach for precision treatment recommendations: application of prostate cancer. Expert Systems with Applications. Vol. 15, 16-26.   

Zheng, J, Gao, L, Zhang, H, Zhu, D, Wang, H, Gao, Q and Leung, V. Joint energy management and interference coordination with Max-Min fairness in ultra-dense hetnets. IEEE Access, Vol. 6, 32588-32600. 

Wang, L, Zhu, D, Towner, E and Dong, M (2018) Obesity risk factors ranking using multi-task learning. IEEE Conference on Biomedical and Health Informatics (IEEE-BHI 2018), Las Vegas, March, 2018. 

Li, X and Zhu, D (2018) Robust feature selection via l 2, 1 -norm in finite mixture of regression. Pattern Recognition Letters, https://doi.org/10.1016/j.patrec.2018.02.021.

Wang, L, Zhu, D and Dong, M (2018) Clustering over-dispersed data with mixed feature types. Statistical Analysis and Data mining, 11(2), 55-65, April 2018.

Li, X, Zhu, D and Dong, M (2018) Multinomial classification with class-conditional overlapping sparse feature groups. Pattern Recognition Letters, vol 101, Jan. 2018, pp 37-43 Source Code.