Publications
Publications @ Cedars-Sinai:
Huixin Zhan and Zijun Frank Zhang, “DYNA: Disease-Specific Language Model for Variant Pathogenicity,” Under 2nd round peer review at Nature Machine Intelligence (arXiv preprint arXiv:2406.00164). 2024. (Link)
Huixin Zhan , Ying Nian Wu, and Zijun Frank Zhang, “Efficient and Scalable Fine-Tune of Language Models for Genome Understanding,” Under 2nd round peer review at Nature Communications (arXiv preprint arXiv:2402.08075). 2024. (Link)
Huixin Zhan , Zijun Frank Zhang, and Jason Moore, “DREAM-VEP: A Modular and Open-Source Framework for Developing Robust, Efficient, and Adaptive Models in Variant Effect Prediction,” Submitted to The 39th Annual AAAI Conference on Artificial Intelligence (AAAI). 2024.
Huixin Zhan and Zijun Frank Zhang, “Parameter-Efficient Fine-Tune on Open Pre-trained Transformers for Genomic Sequence’’ in the 1st Workshop on Generative AI and Biology @ the Proceedings of the 37th Conference on Neural Information Processing Systems (GenBio@NeurIPS). 2023. (Link)
Huixin Zhan and Zijun Frank Zhang, “ProPath: Disease-Specific Protein Language Model for Variant Pathogenicity” in Proceedings of Machine Learning in Computational Biology (MLCB). 2023. (Accepted) (pdf)
Publications @ TTU:
Huixin Zhan, Kun Zhang, Zhong Chen, and Victor S. Sheng, “Defense Against Adversarial Attacks for Neural Representations of Text’’ in Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS). 2024.
Huixin Zhan, Kun Zhang, Zhong Chen, and Victor S. Sheng, “Simplex2vec Backward: From Vectors Back to Simplicial Complex’’ in Proceedings of the 32nd ACM International Conference on Information & Knowledge Management (CIKM). 2023. (Short paper)
Huixin Zhan, Liyuan Gao, Kun Zhang, Zhong Chen, and Victor S. Sheng, “Defending the Graph Reconstruction Attacks for Simplicial Neural Networks’’ in the 5th Edition of Special Session on Private, Secure, and Trust Data Analytics @ the Proceedings of the 10th IEEE International Conference on Data Science and Advanced Analytics (PSTDA@DSAA). 2023.
Huixin Zhan, Kun Zhang, Keyi Lu, and Victor S. Sheng, “Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks’’ in Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI). 2023. Student Abstract Finalists
Huixin Zhan and Victor S. Sheng, “Privacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes’’ in Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI). 2023.
Liyuan Gao, Huixin Zhan, Austin Chen, and Victor S. Sheng, “Towards Fair and Selectively Privacy-Preserving Models Using Negative Multi-Task Learning’’ in Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI). 2023.
Liyuan Gao, Huixin Zhan, Austin Chen, and Victor S. Sheng, “Mitigate Gender Bias using Negative Multi-Task Learning’’ in Neural Processing Letters. 2023.
Huixin Zhan, Kun Zhang, Chenyi Hu, and Victor S. Sheng, “New Threats to Privacy-Preserving Text Representations.’’ in Proceedings of the 2022 Hawaii International Conference on System Sciences (HICSS), pp. 768-777. 2022.
Zhong Chen, Huixin Zhan, Victor Sheng, Andrea Edwards, and Kun Zhang, “Projection Dual Averaging Based Second-order Online Learning.’’ in Proceedings of the 22nd IEEE International Conference on Data Mining (ICDM). 2022.
Zhong Chen, Huixin Zhan, Victor Sheng, Andrea Edwards, and Kun Zhang, “Proximal Cost-sensitive Sparse Group Online Learning.’’ in Proceedings of the IEEE International Conference on Big Data (Big Data). 2022.
Aaron Moody, Chenyi Hu, Huixin Zhan, Makenzie Spurling, and Victor Sheng, “Towards Explainable Summary of Crowdsourced Reviews Through Text Mining.’’ in Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU). 2022.
Makenzie Spurling, Chenyi Hu, Huixin Zhan, and Victor Sheng, “Anomaly Detection in Crowdsourced Work with Interval-Valued Labels.’’ in Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU). 2022.
Huixin Zhan, Kun Zhang, Chenyi Hu, and Victor S. Sheng, “Multi-objective Privacy-preserving Text Representation Learning.’’ in Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM). 2021. (short paper)
Huixin Zhan, Victor S. Sheng, and Wei-Ming Lin, “Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs.’’ in Expert Systems with Applications (2021): 115717.
Huixin Zhan, Kun Zhang, Chenyi Hu, and Victor S. Sheng, “HGATs: Hierarchical Graph Attention Networks for Multiple Comments Integration.’’ in Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 159-163. 2021.
Huixin Zhan, Kun Zhang, Chenyi Hu, and Victor S. Sheng, “Gated Graph Neural Networks (GG-NNs) for Abstractive Multi-comment Summarization.’’ in Proceedings of the 12th IEEE International Conference on Big Knowledge (ICBK). 2021.
Huixin Zhan, Kun Zhang, Chenyi Hu, and Victor S. Sheng, “K2-GNN: Multiple Users’ Comments Integration with Probabilistic K-Hop Knowledge Graph Neural Networks.’’ in Proceedings of the Asian Conference on Machine Learning (ACML), pp. 1477-1492. 2021.
Ireddy Siddhartha, Huixin Zhan, and Victor Sheng, “Abstractive Text Summarization via Stacked LSTM.’’ in Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI). 2021.
Makenzie Spurling, Chenyi Hu, Huixin Zhan, and Victor Sheng, “Estimating Crowd-Worker’s Reliability with Interval-Valued Labels to Improve the Quality of Crowdsourced Work.’’ in Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI). 2021.
Publications @ UTSA:
Huixin Zhan, Wei-Ming Lin and Yongcan Cao, “Deep Model Compression Via Two-Stage Deep Reinforcement Learning’’, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD). 2021. (Link)
Huixin Zhan, Feng Tao and Yongcan Cao, “Human-guided Robot Behavior Learning: A GAN-assisted Preference-based Reinforcement Learning Approach’’, IEEE Robotics and Automation Letters (RA-L), 6, no. 2, 2021: 3545-3552. (pdf)
Huixin Zhan and Yongcan Cao, “Efficient Multi-objective Reinforcement Learning via Multiple-Gradient Descent with Iteratively Discovered Weight-Vector Sets’’, Journal of Artificial Intelligence Research 70 (JAIR), 2021: 319-349. (pdf)
Huixin Zhan, Yongcan Cao, Manuel Cortez and Anthony Harris, “Proactive Data-driven UAV State Estimation via Online End-to-end Learning’’, AIAA Scitech 2020 Forum, p. 1090. 2020. (pdf)