Foundational AI/Machine Learning Research (my student as the first author)
- [WACV-25] Li, C, Zhu, H, Sultan, R, Khanduri, P, Qiang, Y, Chetty, I, Thind, K, and Zhu, D. MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training.
- [WACV-25] Li, C, Khanduri, P, Qiang, Y, Sultan, R, Chetty, I and Zhu, D. AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation.
- [ECCV-24] Qiang, Y, Li, C, Khanduri, P, and Zhu, D. Fairness-aware Vision Transformer via Debiased Self-Attention. In the proceedings of the 2024 European Conference on Computer Vision (ECCV-24). Accept rate is 2,395/8,585 = 27.9%.
- [TheWebConf-24] Zamiri, M, Qiang, Y, Nikolaev, F, Zhu, D, Kotov, A. 2024. Benchmark and Neural Architecture for Conversational Entity Retrieval from a Knowledge Graph. In the proceedings of the 2024 ACM Web Conference. Accept rate is 806/4,028 = 20.2%.
- [CHI-24] Zheng, W., Walquist, E., Datey, I., Zhou, X., Berishaj, K., Mcdonald, M., Parkhill, M., Zhu, D., & Zytko, D. 2024. It's not what we were trying to get at, but I think maybe it should be”: Learning how to do trauma-informed design with a data donation platform for online dating sexual violence. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11–16, 2024, Honolulu, HI, USA (pp. 1-15). ACM. https://doi.org/10.1145/3613904.3642045. Accept rate is 1,060/4,028 = 26.3%.
- [AAAI-24] Zhu, Z., Chen, H., Zhang, J., Wang, X., Jin, Z., Xue, M., Zhu, D. and Choo, K.K.R., 2023. MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks. In the Proceedings of Thirty-Seventh AAAI Conference on Artificial Intelligence. Accept rate: 2,342/12,100 = 23.75%.
- [MICCAI-23] Li, C., Qiang, Y, Sultan, R, Bagher-Ebadian, P, Khanduri, V, Chetty, IJ, and Zhu, D. FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images. In the Proceedings of 26th International Conference on Medical Image Computing and Computer Assisted Intervention, Vancouver, Canada. Accept rate: 740/2,250 = 32%.
- [AdvML@ICML-23] Khanduri, P, Li, C, Sultan, R, Qiang, Y and Zhu, D. (2023) Proximal Composite Optimization for Distributionally Robust Learning. In the proceedings of new frontiers in adversarial machine learning (AdvML) workshop at ICML, 2023.
- [IJCAI-23] Li, X, Pan, D, Li, C, Qiang, Y, and Zhu, D. Negative Flux Aggregation to Estimate Feature Attributions. In the Proceedings of 32st International Joint Conference on Artificial Intelligence, Marco, China. Acceptance rate: 685/4,566 = 15%.
- [AAAI-23] Li, X., Li, X., Pan, D, Qiang, Y., and Zhu, D. (2023) Learning Compact Features via In-Training Representation Alignment. In the Proceedings of Thirty-Seventh AAAI Conference on Artificial Intelligence. Accept rate: 1,721/8,777=19.6%.
- [NuerIPS-22] Qiang, Y, Pan, D, Li, C, Li, X, Jang, R, and Zhu, D. (2022) AttCAT: Explaining Transformers via Attentive Class Activation Tokens. In the Proceedings of Thirty-sixth Conference on Neural Information Processing Systems. Acceptance rate: 2,665/10,411 = 25.6%.
- [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 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).
- [IJCAI-21] Pan, D, Li, X and Zhu, D. (2021) Explaining Deep Neural Network Models with Adversarial Gradient Integration. Proceedings of 30th International Joint Conference on Artificial Intelligence, Montreal, Canada. Acceptance rate: 587/4,204=13.9%.
- [DMKD] 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.
- [AAAI-21] Li, X, Li, X, Pan,D and Zhu, D. (2021) Improving adversarial robustness via probabilistically compact loss with logit constraints. Proceedings of Thirty-Five AAAI Conference on Artificial Intelligence, virtual conference. Code Acceptance rate: 1,692/7,911=21.4%
- [IJCAI-20] Pan, D, Li, X, Li, X and Zhu, D. (2020) Explainable recommendation via interpretable feature mapping and evaluating explainability. Proceedings of 29th International Joint Conference on Artificial Intelligence, Yokohama, Japan. Acceptance rate: 592/4,717=12.6%
- [IJCNN-20] Qiang, Y, Li, X and Zhu, D. (2020) Toward tag-free aspect based sentiment analysis: a multiple attention network approach. Proceedings of International Joint Conference on Neural Networks, Glasgow, Scotland, UK.
- [AAAI-20] Li, X, Li, X, Pan,D and Zhu, D. (2020) On the learning behavior of logistic and softmax losses for deep neural networks. In the proceedings of Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, USA. Acceptance rate: 1,591/7,737=20.6%.
- [PRL] Li, X and Zhu, D. (2018) Robust feature selection via l 2, 1 -norm in finite mixture of regression. Pattern Recognition Letters, 108 (2018) 15–22.
- [SAM] Wang, L, Zhu, D. and Dong, M (2018) Clustering over-dispersed data with mixed feature types. Statistical Analysis and Data mining, 2018;11:55–65.
- [PRL] 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.
- [ICDM-17] Wang, L, Li, Y, Zhou, J, Zhu, D and Ye, J (2017) Multi-task Survival Analysis. Proceedings of 2017 IEEE International Conference on Data Mining. Acceptance rate: 72/778=9.25%.