Dr Nan Ye

Senior Lecturer

School of Mathematics and Physics
Faculty of Science
nan.ye@uq.edu.au
+61 7 334 69095

Overview

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/.

Research Interests

  • machine learning
  • sequential decision making
  • numerical optimization

Publications

View all Publications

Grants

View all Grants

Supervision

  • Doctor Philosophy

  • Doctor Philosophy

  • Doctor Philosophy

View all Supervision

Publications

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 Article

Conference Publication

  • 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

  • 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.

  • 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

  • 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.

  • 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

  • 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

  • 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

  • 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.

  • 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

Grants (Administered at UQ)

PhD and MPhil Supervision

Current Supervision

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

    Other advisors:

  • Doctor Philosophy — Principal Advisor

  • Doctor Philosophy — Associate Advisor

    Other advisors:

  • Doctor Philosophy — Associate Advisor

    Other advisors: