Dr Nan Ye
Senior Lecturer
School of Mathematics and Physics
+61 7 334 69095
Building 69, Room 616
Researcher biography
Nan Ye's research interest spans machine learning, statistics and optimization. He has published papers on topics including sequential decision making under uncertainty, weakly supervised learning, probabilistic graphical models, statistical learning theory, in venues such as NeurIPS, ICML, ICLR, UAI, JAIR, JMLR. He received an IJCAI-JAIR Best Paper Prize in 2022, and a UAI Best Student Paper Award in 2014.
He is a Lecturer in Statistics and Data Science in the School of Mathematics and Physics in University of Queensland. He previously held postdoc positions at QUT and UC Berkeley from 2015 to 2018, and at NUS from 2013 to 2014. He obtained his PhD in Computer Science from NUS, and completed double first-class honors in Computer Science and Applied Mathematics, also from NUS.
Please visit his personal webpage for more information: https://yenan.github.io/.
Book Chapter
Ye, Nan, Roosta-Khorasani, Farbod and Cui, Tiangang (2019). Optimization methods for inverse problems. 2017 MATRIX annals. (pp. 121-140) edited by David R. Wood, Jan de Gier, Cheryl E. Praeger and Terence Tao. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-04161-8_9
Journal Articles
Zhao, Yawen, Zhang, Mingzhe, Zhang, Chenhao, Chen, Weitong, Ye, Nan and Xu, Miao (2024). A boosting framework for positive-unlabeled learning. Statistics and Computing, 35 (1) 2. doi: 10.1007/s11222-024-10529-y
Lei, Yeming, Zhou, Shijie and Ye, Nan (2024). Structured neural networks for CPUE standardization: A case study of the blue endeavour prawn in Australia's Northern Prawn Fishery. Fisheries Research, 279 107140, 107140. doi: 10.1016/j.fishres.2024.107140
Snoswell, Aaron J., Snoswell, Centaine L. and Ye, Nan (2024). Eliciting patient preferences and predicting behaviour using Inverse Reinforcement Learning for telehealth use in outpatient clinics. Frontiers in Digital Health, 6 1384248, 1384248. doi: 10.3389/fdgth.2024.1384248
Lei, Yeming, Zhou, Shijie and Ye, Nan (2024). Spatial-temporal neural networks for catch rate standardization and fish distribution modeling. Fisheries Research, 278 107097, 107097. doi: 10.1016/j.fishres.2024.107097
Hoerger, Marcus, Kurniawati, Hanna, Kroese, Dirk and Ye, Nan (2024). Adaptive discretization using Voronoi trees for continuous pOMDPs. The International Journal of Robotics Research, 43 (9), 1283-1298. doi: 10.1177/02783649231188984
Evans, David and Ye, Nan (2023). Blockwise acceleration of alternating least squares for canonical tensor decomposition. Numerical Linear Algebra with Applications, 30 (6) e2516. doi: 10.1002/nla.2516
Lei, Yeming, Zhou, Shijie, Filar, Jerzy and Ye, Nan (2023). Multi-pass Bayesian estimation: a robust Bayesian method. Computational Statistics, 39 (4), 2183-2216. doi: 10.1007/s00180-023-01390-0
Ju, Jun, Kurniawati, Hanna, Kroese, Dirk and Ye, Nan (2023). Model‐based offline reinforcement learning for sustainable fishery management. Expert Systems, 42 (1) e13324. doi: 10.1111/exsy.13324
Yu, Xinguo, Song, Wu, Lyu, Xiaopan, He, Bin and Ye, Nan (2020). Reading both single and multiple digital video clocks using context-aware pixel periodicity and deep learning. International Journal of Digital Crime and Forensics, 12 (2), 21-39. doi: 10.4018/IJDCF.2020040102
Mitchell, Drew, Ye, Nan and De Sterck, Hans (2020). Nesterov acceleration of alternating least squares for canonical tensor decomposition: Momentum step size selection and restart mechanisms. Numerical Linear Algebra with Applications, 27 (4) e2297. doi: 10.1002/nla.2297
Avrachenkov, Konstantin, Prałat, Paweł and Ye, Nan (2019). Preface. Lecture Notes in Computer Science, 11631 LNCS.
Yu, Xinguo, Wang, Mingshu, Gan, Wenbin, He, Bin and Ye, Nan (2018). A framework for solving explicit arithmetic word problems and proving plane geometry theorems. International Journal of Pattern Recognition and Artificial Intelligence, 33 (7) 1940005, 1940005. doi: 10.1142/S0218001419400056
Ye, Nan, Somani, Adhiraj, Hsu, David and Lee, Wee Sun (2017). DESPOT: Online POMDP Planning with Regularization. The Journal of Artificial Intelligence Research, 58, 231-266. doi: 10.1613/jair.5328
Nguyen Viet Cuong, Ye, Nan, Lee, Wee Sun and Chieu, Hai Leong (2014). Conditional random field with high-order dependencies for sequence labeling and segmentation. Journal of Machine Learning Research, 15, 981-1009.
Jain, Sanjay, Stephan, Frank and Nan, Ye (2008). Prescribed learning of indexed families. Fundamenta Informaticae, 83 (1-2), 159-175.
Conference Papers
Jiang, Wei, Xiong, Zhuang, Liu, Feng, Ye, Nan and Sun, Hongfu (2024). Fast controllable diffusion models for undersampled MRI reconstruction. 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 27-30 May 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/isbi56570.2024.10635891
Wilton, Jonathan and Ye, Nan (2024). Robust loss functions for training decision trees with noisy labels. Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), Vancouver, BC, Canada, 20 - 28 February 2024. Washington, DC, United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v38i14.29516
Hoerger, Marcus, Kurniawati, Hanna, Kroese, Dirk and Ye, Nan (2024). A surprisingly simple continuous-action POMDP solver: lazy cross-entropy search over policy trees. 38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence, Vancouver, Canada, 20-27 February 2024. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v38i18.29992
Hoerger, Marcus, Kurniawati, Hanna, Kroese, Dirk and Ye, Nan (2022). Adaptive Discretization Using Voronoi Trees for Continuous-Action POMDPs. Fifteenth Workshop on the Algorithmic Foundations of Robotics WAFR 2022, College Park, MD United States, 22-24 June 2022. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-21090-7_11
Wilton, Jonathan, Koay, Abigail M. Y., Ko, Ryan K. L., Miao Xu and Ye, Nan (2022). Positive-unlabeled learning using random forests via recursive greedy risk minimization. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, United States, 29 November - 1 December 2022. New Orleans, LA, United States: Neural information processing systems foundation.
Ju, Jun, Kurniawati, Hanna, Kroese, Dirk and Ye, Nan (2021). MOOR: Model-based offline reinforcement learning for sustainable fishery management. 24th International Congress on Modelling and Simulation, Sydney, NSW, Australia, 5 - 10 December 2021. Sydney, NSW, Australia: International Congress on Modelling and Simulation. doi: 10.36334/modsim.2021.M2.ju
Lei, Y., Zhou, S. and Ye, N. (2021). Prior versus data: A new Bayesian method for fishery stock assessment. 24th International Congress on Modelling and Simulation, Sydney, NSW, Australia, 5 - 10 December 2021. Sydney, NSW, Australia: International Congress on Modelling and Simulation. doi: 10.36334/modsim.2021.A1.lei
Snoswell, Aaron J., Singh, Surya P. N. and Ye, Nan (2020). Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, ACT Australia, 1-4 December 2020. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/SSCI47803.2020.9308391
Nguyen, Thanh Tan, Ye, Nan and Bartlett, Peter (2020). Greedy convex ensemble. Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20), Online, 7-15 January 2021. Palo Alto, CA United States: A A A I Press. doi: 10.24963/ijcai.2020/429
Ma, Xiao, Karkus, Peter, Hsu, David, Lee, Wee Sun and Ye, Nan (2020). Discriminative particle filter reinforcement learning for complex partial observations. ICLR 2020: Eighth International Conference on Learning Representations, Virtual, 26 April - 1 May 2020. International Conference on Learning Representations, ICLR.
Filar, Jerzy A., Qiao, Zhihao and Ye, Nan (2019). POMDPs for sustainable fishery management. International Congress on Modelling and Simulation, Canberra, Australia, 1-6 December 2019. Modelling and Simulation Society of Australia and New Zealand. doi: 10.36334/modsim.2019.g2.filar
Snoswell, A. J., Singh, S. P. N. and Ye, N. (2019). Maximum entropy approaches for inverse reinforcement learning. INFORMS-APS, Brisbane, Australia, 3-5 July 2019.
Mengersen, Kerrie, Peterson, Erin E., Clifford, Samuel, Ye, Nan, Kim, June, Bednarz, Tomasz, Brown, Ross, James, Allan, Vercelloni, Julie, Pearse, Alan R., Davis, Jacqueline and Hunter, Vanessa (2017). Modelling imperfect presence data obtained by citizen science. 26th Annual Conference of the International-Environmetrics-Society (TIES), Riccarton, Scotland, 18-22 July 2016. Oxford, United Kingdom: John Wiley & Sons. doi: 10.1002/env.2446
Wrigley, Andrew, Lee, Wee Sun and Ye, Nan (2017). Tensor belief propagation. 34th International Conference on Machine Learning, Sydney, NSW, Australia, 6-11 August 2017. San Diego, CA, United States: JMLR.org.
Cuong, Nguyen Viet, Ye, Nan and Lee, Wee Sun (2016). Robustness of Bayesian pool-based active learning against prior misspecification. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, AZ, United States, 12-17 February 2016. Palo Alto, CA, United States: AAAI Press.
Bai, Haoyu, Cai, Shaojun, Ye, Nan, Hsu, David and Lee, Wee Sun (2015). Intention-aware online POMDP planning for autonomous driving in a crowd. 2015 IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA United States, 26-30 May 2015. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICRA.2015.7139219
Nguyen Viet Cuong, Lee, Wee Sun and Ye, Nan (2014). Near-optimal adaptive pool-based active learning with general loss. 30th Conference on Uncertainty in Artificial Intelligence (UAI), Quebec City, Canada, 23-27 July 2014. Arlington, VA, United States: AUAI Press.
Ding, Wan, Yu, Xinguo and Ye, Nan (2014). Goal detection for broadcast basketball video using superimposed texts: A transition pattern approach. ICIMCS '14: International Conference on Internet Multimedia Computing and Service, Xiamen, China, 10-12 July 2014. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/2632856.2632859
Somani, Adhiraj, Ye, Nan, Hsu, David and Lee, Wee Sun (2013). DESPOT: Online POMDP planning with regularization. Advances in Neural Information Processing Systems 26 (NIPS 2013), Lake Tahoe, NV, United States, 5-10 December 2013. Neural information processing systems foundation.
Nguyen, Viet Cuong, Lee, Wee Sun, Ye, Nan, Chai, Kian Ming A. and Chieu, Hai Leong (2013). Active learning for probabilistic hypotheses using the maximum Gibbs error criterion. NIPS'13: 26th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, United States, 5-10 December 2013. Red Hook, NY, United States: Curran Associates. doi: 10.5555/2999611.2999774
Ye, Nan, Chai, Kian Ming A., Lee, Wee Sun and Chieu, Hai Leong (2012). Optimizing F-measures: A tale of two approaches. 29th International Conference on Machine Learning, ICML 2012, Edinburgh, United Kingdom, 26 June - 1 July 2012. New York, NY United States: Association for Computing Machinery.
Ye, Nan, Lee, Wee Sun, Chieu, Hai Leong and Wu, Dan (2009). Conditional random fields with high-order features for sequence labeling. 23rd Annual Conference on Neural Information Processing Systems 2009, Vancouver, Canada, 7-10 December 2009. Curran Associates.
Jain, Sanjay, Stephan, Frank and Ye, Nan (2009). Learning from streams. 20th International Conference of Algorithmic Learning Theory ALT 2009, Porto, Portugal, 3-5 October 2009. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-04414-4_28
Jain, Sanjay, Stephan, Frank and Ye, Nan (2009). Prescribed learning of r.e. classes. 18th International Conference on Algorithmic Learning Theory, Sendai, Japan, 1-4 October 2007. Amsterdam, Netherlands: Elsevier. doi: 10.1016/j.tcs.2009.01.011
Wu, Dan, Lee, Wee Sun, Ye, Nan and Chieu, Hai Leong (2009). Domain adaptive bootstrapping for named entity recognition. 2009 Conference on Empirical Methods in Natural Language Processing, Singapore, 6 - 7 August 2009. Stroudsburg, PA United States: Association for Computational Linguistics. doi: 10.3115/1699648.1699699
Ning, Kang, Ye, Nan and Leong, Hon Wai (2008). On preprocessing and antisymmetry in de novo peptide sequencing: Improving efficiency and accuracy. Computational Systems Bioinformatics 2007, San Diego, CA United States, 13-17 August 2007. London, United Kingdom: World Scientific Publishing. doi: 10.1142/S0219720008003503
Jain, Sanjay, Stephan, Frank and Ye, Nan (2007). Prescribed learning of R.E. classes. 18th International Conference on Algorithmic Learning Theory, Sendai Japan, 1-4 October 2007. Berlin, Germany: Springer. doi: 10.1007/978-3-540-75225-7_9