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2020 Journal Article Predicting alcohol dependence treatment outcomes: A prospective comparative study of clinical psychologists vs ‘trained’ machine learning modelsSymons, Martyn, Feeney, Gerald F. X., Gallagher, Marcus R., Young, Ross Mc D. and Connor, Jason P. (2020). Predicting alcohol dependence treatment outcomes: A prospective comparative study of clinical psychologists vs ‘trained’ machine learning models. Addiction, 115 (11) add.15038, 2164-2175. doi: 10.1111/add.15038 |
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2020 Conference Publication Fitness landscape features and reward shaping in reinforcement learning policy spacesdu Preez-Wilkinson, Nathaniel and Gallagher, Marcus (2020). Fitness landscape features and reward shaping in reinforcement learning policy spaces. Parallel Problem Solving from Nature – PPSN XVI, Leiden, The Netherlands, 5 - 9 September 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-58115-2_35 |
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2020 Conference Publication A novel mutation operator for variable length algorithmsVan Ryt, Saskia, Gallagher, Marcus and Wood, Ian (2020). A novel mutation operator for variable length algorithms. AI 2020: Advances in Artificial Intelligence: 33rd Australasian Joint Conference, Canberra, ACT, Australia, 29 - 30 November 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-64984-5_14 |
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2020 Conference Publication An Implementation and Experimental Evaluation of a Modularity Explicit Encoding Method for Neuroevolution on Complex Learning TasksQiao, Yukai and Gallagher, Marcus (2020). An Implementation and Experimental Evaluation of a Modularity Explicit Encoding Method for Neuroevolution on Complex Learning Tasks. 33rd Australasian Joint Conference, AI 2020, Canberra, ACT Australia, 29–30 November 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-64984-5_11 |
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2020 Book AI 2020: advances in artificial intelligenceMarcus Gallagher, Nour Moustafa and Erandi Lakshika eds. (2020). AI 2020: advances in artificial intelligence. Lecture Notes in Computer Science, Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-64984-5 |
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2019 Journal Article Network analysis and visualisation of opioid prescribing dataHu, Xuelei, Gallagher, Marcus, Loveday, William, Dev, Abhilash and Connor, Jason P. (2019). Network analysis and visualisation of opioid prescribing data. IEEE Journal of Biomedical and Health Informatics, 24 (5) 8822723, 1-9. doi: 10.1109/jbhi.2019.2939028 |
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2019 Conference Publication Reversible jump probabilistic programmingRoberts, David A., Gallagher, Marcus and Taimre, Thomas (2019). Reversible jump probabilistic programming. The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019), Naha, Okinawa, Japan, 16 - 18 April 2019. Brookline, MA, United States: ML Research Press. |
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2019 Journal Article Machine learning vs addiction therapists: a pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medicationSymons, Martyn, Feeney, Gerald F.X., Gallagher, Marcus R., Young, Ross McD. and Connor, Jason P. (2019). Machine learning vs addiction therapists: a pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medication. Journal of Substance Abuse Treatment, 99, 156-162. doi: 10.1016/j.jsat.2019.01.020 |
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2019 Journal Article Quantitative measure of nonconvexity for black-box continuous functionsTamura, Kenichi and Gallagher, Marcus (2019). Quantitative measure of nonconvexity for black-box continuous functions. Information Sciences, 476, 64-82. doi: 10.1016/j.ins.2018.10.009 |
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2019 Conference Publication Exploring the MLDA benchmark on the Nevergrad platformRapin, Jeremy, Gallagher, Marcus, Kerschke, Pascal, Preuss, Mike and Teytaud, Olivier (2019). Exploring the MLDA benchmark on the Nevergrad platform. 2019 Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, 13 - 17 July 2019. New York, New York, USA: Association for Computing Machinery, Inc. doi: 10.1145/3319619.3326830 |
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2019 Conference Publication Fitness landscape analysis in data-driven optimization: An investigation of clustering problemsGallagher, Marcus (2019). Fitness landscape analysis in data-driven optimization: An investigation of clustering problems. IEEE Congress on Evolutionary Computation (IEEE CEC), Wellington, New Zealand, 10-13 June, 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2019.8790323 |
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2019 Conference Publication Exchangeability and kernel invariance in trained MLPsTsuchida, Russell, Roosta, Fred and Gallagher, Marcus (2019). Exchangeability and kernel invariance in trained MLPs. Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19, Macao, China, 10-16 August 2019. Marina del Rey, CA USA: International Joint Conferences on Artificial Intelligence. doi: 10.24963/ijcai.2019/498 |
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2018 Conference Publication Intra-task curriculum learning for faster reinforcement learning in video gamesdu Preez-Wilkinson, Nathaniel, Gallagher, Marcus and Hu, Xuelei (2018). Intra-task curriculum learning for faster reinforcement learning in video games. 31st Australasian Joint Conference on Artificial Intelligence (AI 2018), Wellington, New Zealand, 11-14 December 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_6 |
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2018 Journal Article Direct feature evaluation in black-box optimization using problem transformationsSaleem, Sobia, Gallagher, Marcus and Wood, Ian (2018). Direct feature evaluation in black-box optimization using problem transformations. Evolutionary Computation, 27 (1), 75-98. doi: 10.1162/evco_a_00247 |
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2018 Conference Publication A model-based framework for black-box problem comparison using gaussian processesSaleem, Sobia, Gallagher, Marcus and Wood, Ian (2018). A model-based framework for black-box problem comparison using gaussian processes. 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018, Coimbra, Portugal, 8-12 September 2018. Cham, Switzerland: Springer Verlag. doi: 10.1007/978-3-319-99259-4_23 |
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2018 Conference Publication Flood-fill Q-learning updates for learning redundant policies in order to interact with a computer screen by clickingdu Preez-Wilkinson, Nathaniel, Gallagher, Marcus and Hu, Xuelei (2018). Flood-fill Q-learning updates for learning redundant policies in order to interact with a computer screen by clicking. 31st Australasian Joint Conference on Artificial Intelligence, AI 2018, Wellington,, December 11, 2018-December 14, 2018. Germany: Springer Verlag. doi: 10.1007/978-3-030-03991-2_49 |
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2018 Conference Publication Invariance of weight distributions in rectified MLPsTsuchida, Russell, Roosta-Khorasani, Farbod and Gallagher, Marcus (2018). Invariance of weight distributions in rectified MLPs. 35th International Conference on Machine Learning, Stockholm, Sweden, 10-15 July 2018. Cambridge, MA, United States: M I T Press. |
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2017 Journal Article Parallel evolutionary algorithm for single and multi-objective optimisation: Differential evolution and constraints handlingPedroso, Dorival M., Bonyadi, Mohammad Reza and Gallagher, Marcus (2017). Parallel evolutionary algorithm for single and multi-objective optimisation: Differential evolution and constraints handling. Applied Soft Computing, 61, 995-1012. doi: 10.1016/j.asoc.2017.09.006 |
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2017 Journal Article Multiple community energy storage planning in distribution networks using a cost-benefit analysisSardi, Junainah, Mithulananthan, N., Gallagher, M. and Hung, Duong Quoc (2017). Multiple community energy storage planning in distribution networks using a cost-benefit analysis. Applied Energy, 190, 453-463. doi: 10.1016/j.apenergy.2016.12.144 |
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2017 Journal Article Use of freely available datasets and machine learning methods in predicting deforestationMayfield, Helen, Smith, Carl, Gallagher, Marcus and Hockings, Marc (2017). Use of freely available datasets and machine learning methods in predicting deforestation. Environmental Modelling and Software, 87, 17-28. doi: 10.1016/j.envsoft.2016.10.006 |
