The University of Queensland

Optimization methods for inverse problems

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

Blockwise acceleration of alternating least squares for canonical tensor decomposition

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

Multi-pass Bayesian estimation: a robust Bayesian method

Lei, Yeming, Zhou, Shijie, Filar, Jerzy and Ye, Nan (2023). Multi-pass Bayesian estimation: a robust Bayesian method. Computational Statistics. doi: 10.1007/s00180-023-01390-0

Model‐based offline reinforcement learning for sustainable fishery management

Ju, Jun, Kurniawati, Hanna, Kroese, Dirk and Ye, Nan (2023). Model‐based offline reinforcement learning for sustainable fishery management. Expert Systems. doi: 10.1111/exsy.13324

Reading both single and multiple digital video clocks using context-aware pixel periodicity and deep learning

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

Nesterov acceleration of alternating least squares for canonical tensor decomposition: Momentum step size selection and restart mechanisms

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

Preface

Avrachenkov, Konstantin, Prałat, Paweł and Ye, Nan (2019). Preface. Lecture Notes in Computer Science, 11631 LNCS.

A framework for solving explicit arithmetic word problems and proving plane geometry theorems

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

DESPOT: Online POMDP Planning with Regularization

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

Conditional random field with high-order dependencies for sequence labeling and segmentation

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.

Prescribed learning of indexed families

Jain, Sanjay, Stephan, Frank and Nan, Ye (2008). Prescribed learning of indexed families. Fundamenta Informaticae, 83 (1-2), 159-175.

Adaptive Discretization Using Voronoi Trees for Continuous-Action POMDPs

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

Positive-unlabeled learning using random forests via recursive greedy risk minimization

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.

MOOR: Model-based offline reinforcement learning for sustainable fishery management

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

Prior versus data: A new Bayesian method for fishery stock assessment

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

Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and Algorithms

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

Discriminative particle filter reinforcement learning for complex partial observations

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.

Greedy convex ensemble

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

Maximum entropy approaches for inverse reinforcement learning

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.

POMDPs for sustainable fishery management

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

Modelling imperfect presence data obtained by citizen science

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

Tensor belief propagation

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.

Robustness of Bayesian pool-based active learning against prior misspecification

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.

Intention-aware online POMDP planning for autonomous driving in a crowd

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

Near-optimal adaptive pool-based active learning with general loss

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.

Goal detection for broadcast basketball video using superimposed texts: A transition pattern approach

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

Active learning for probabilistic hypotheses using the maximum Gibbs error criterion

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

DESPOT: Online POMDP planning with regularization

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.

Optimizing F-measures: A tale of two approaches

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.

Conditional random fields with high-order features for sequence labeling

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.

Learning from streams

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

Prescribed learning of r.e. classes

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

Domain adaptive bootstrapping for named entity recognition

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

On preprocessing and antisymmetry in de novo peptide sequencing: Improving efficiency and accuracy

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

Prescribed learning of R.E. classes

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