Note: Asterisk * indicates publications on tenure track. Circle o indicates PI’s students or interns. Dagger   indicates publications as a mentor of PI’s students or interns. PI’s name is bold

[12] Frazier Baker◦, Ziqi Chen◦, and Xia Ning*†. RLSynC: Offline-online reinforcement learning for synthon completion. In Molecular Machine Learning Workshop, MIT, October 2023.

[11] Ziqi Chen◦, Oluwatosin R. Ayinde, James R. Fuchs, Huan Sun, and Xia Ning*†. G2Retro: Two-step graphgenerative models for retrosynthesis prediction. In Molecular Machine Learning Workshop, MIT, October 2023.

[10] Vishal Dey and Xia Ning*. Precision anti-cancer drug selection via neural ranking. In 22nd International Workshop on Data Mining in Bioinformatics (BIOKDD 2023), Aug 2023.

[9] Daniele Pala, Brian Lee, Xia Ning*, Dokyoon Kim, and Li Shen. Mediation analysis and mixed-effects models for the identification of stage-specific imaging genetics patterns in Alzheimer’s disease. In Artificial Intelligence Techniques for BioMedicine and HealthCare workshop, International Conference on Bioinformatics and Biomedicine (BIBM), Nov 2022.

[8] Bo Peng, Zhiyun Ren, Xiaohui Yao, Kefei Liu, Andrew Saykin, Li Shen, and Xia Ning*. Prioritizing amyloid imaging biomarkers in Alzheimer’s disease via learning to rank. In International Workshop On Multimodal Brain Image Analysis (MBIA 2019), in Conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019.

[7] Ziwei Fan and Xia Ning*. Local sparse linear model ensemble for top-n recommendation. In The Third SDM Workshop on Machine Learning Methods for Recommender Systems, MLRec’17, 2017.

[6] Baichuan Zhang, Sutanay Choudhury, Mohammad Al Hasan, Xia Ning*, Khushbu Agarwal, and Sumit Purohi and Paola Pesantez Cabrera.Trust from the past: Bayesian personalized ranking-based link prediction in knowledge graphs. In The Third SDM Workshop on MiningNetworks and Graphs: A Big Data Analytic Challenge, MNG’16, 2016.

[5] Renqiang Min, Xia Ning*, Yanjun Qi, Chao Cheng, Anthony Bonner, and Mark Gerstein. Ensemble learning based sparse high-order Boltzmann machine for unsupervised feature interaction identification. In NIPS Workshop on Machine Learning in Computational Biology, MLCB’15, 2015.

[4] Xia Ning and Guofei Jiang. HLAer: A system for heterogeneous log analysis. In SDM Workshop on Heterogeneous Learning, 2014.

[3] Renqiang Min, Xia Ning, Chao Cheng, and Mark Gerstein. Interpretable sparse high-order Boltzmann machines for transcription factor interaction identification. In NIPS Workshop on Machine Learning in Computational Biology, MLCB’13, 2013.

[2] Xia Ning. Sparse linear methods for top-N recommendation. In NIPS workshop on Women in Machine Learning, 2012.

[1] Xia Ning and George Karypis. The set classification problem and solution methods. In Workshops Proceedings of the 8th IEEE International Conference on Data Mining, ICDM’08, pages 720–729, 2008.