Dr Nan Ye

Lecturer in Statistics&Data Science

School of Mathematics and Physics

+61 7 334 69095

nan.ye@uq.edu.au

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/.

Featured projects	Duration
Fishery Management with POMDPs
Algorithms for Computing Nash Equilibria
Reinforcement learning for playing games
Solving Differential Equations using Machine Learning

Publications

Book Chapter (1)

Journal Articles (4)

Conference Papers (12)

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

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

Conference Papers

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:

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

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.

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

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

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

Areas of research

Operations research

Statistics, probability and data science