Estimating Problem Value in an Intelligent Tutoring System Using Bayesian Networks. - Michael Mayo; Antonija Mitrovic (Australian Joint Conference on Artificial Intelligence, 1999)

Using a Probabilistic Student Model to Control Problem Difficulty. - Michael Mayo; Antonija Mitrovic (Intelligent Tutoring Systems, 2000)

Constraint-Based Tutors: A Success Story. - Antonija Mitrovic; Michael Mayo; Pramuditha Suraweera; Brent Martin (IEA/AIE, 2001)

Using Evaluation to Shape ITS Design: Results and Experiences with SQL-Tutor. - Antonija Mitrovic; Brent Martin; Michael Mayo (User Model. User Adapt. Interact., 2002)

Using Context to Solve the Correspondence Problem in Simultaneous Localisation and Mapping. - Margaret E. Jefferies; Wenrong Weng; Jesse T. Baker; Michael Mayo (PRICAI, 2004)

Using 2D and 3D Landmarks to Solve the Correspondence Problem in Cognitive Robot Mapping. - Margaret E. Jefferies; Michael J. Cree; Michael Mayo; Jesse T. Baker (Spatial Cognition, 2004)

Bayesian Sequence Learning for Predicting Protein Cleavage Points. - Michael Mayo (PAKDD, 2005)

Random Convolution Ensembles. - Michael Mayo (PCM, 2007)

Automatic species identification of live moths. - Michael Mayo; Anna T. Watson (Knowl. Based Syst., 2007)

3D Face Recognition Using Multiview Keypoint Matching. - Michael Mayo; Edmond Zhang (AVSS, 2009)

SIFTing the Relevant from the Irrelevant: Automatically Detecting Objects in Training Images. - Edmond Zhang; Michael Mayo (DICTA, 2009)

Evolving Concurrent Petri Net Models of Epistasis. - Michael Mayo; Lorenzo Beretta 002 (ACIIDS (2), 2010)

Genomic analysis of a rare human tumor. - Steven J. M. Jones; Janessa Laskin; Yvonne Y. Li; Obi L. Griffith; Jianghong An; Mikhail Bilenky; Yaron S. N. Butterfield; Timothee Cezard; Eric Chuah; Richard Corbett; Anthony P. Fejes; Malachi Griffith; et al. (BMC Bioinform., 2010)

Hybridizing Data Stream Mining and Technical Indicators in Automated Trading Systems. - Michael Mayo (MDAI, 2011)

Modelling epistasis in genetic disease using Petri nets, evolutionary computation and frequent itemset mining. - Michael Mayo; Lorenzo Beretta 002 (Expert Syst. Appl., 2011)

Cartesian Genetic Programming for Trading: A Preliminary Investigation. - Michael Mayo (AusDM, 2012)

Evolutionary Data Selection for Enhancing Models of Intraday Forex Time Series. - Michael Mayo (EvoApplications, 2012)

Full model selection in the space of data mining operators. - Quan Sun; Bernhard Pfahringer; Michael Mayo (GECCO (Companion), 2012)

Identifying Market Price Levels Using Differential Evolution. - Michael Mayo (EvoApplications, 2013)

Towards a Framework for Designing Full Model Selection and Optimization Systems. - Quan Sun; Bernhard Pfahringer; Michael Mayo (MCS, 2013)

Predicting Regression Test Failures Using Genetic Algorithm-Selected Dynamic Performance Analysis Metrics. - Michael Mayo; Simon A. Spacey (SSBSE, 2013)

Evolving artificial datasets to improve interpretable classifiers. - Michael Mayo; Quan Sun (IEEE Congress on Evolutionary Computation, 2014)

Alternating model trees. - Eibe Frank; Michael Mayo; Stefan Kramer 001 (SAC, 2015)

An Adaptive Model-Based Mutation Operator for the Wind Farm Layout Optimisation Problem. - Michael Mayo; Maisa Daoud (SMC, 2015)

BlockCopy-based operators for evolving efficient wind farm layouts. - Michael Mayo; Chen Zheng (CEC, 2016)

Proceedings in Adaptation, Learning and Optimization. - Michael Mayo; Maisa Daoud; Chen Zheng (IES, 2016)

Towards a New Evolutionary Subsampling Technique for Heuristic Optimisation of Load Disaggregators. - Michael Mayo; Sara Omranian (PAKDD Workshops, 2016)

Deferral classification of evolving temporal dependent data streams. - Michael Mayo; Albert Bifet (SAC, 2016)

Constructing Document Vectors Using Kernel Density Estimates. - Michael Mayo; Sean Goltz (MDAI, 2017)

Surrogate modeling a computational fluid dynamics-based wind turbine wake simulation using machine learning. - Brett Wilson; Sarah Wakes; Michael Mayo (SSCI, 2017)

Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data. - Michael Mayo; Eibe Frank (CoRR, 2017)

Aesthetic local search of wind farm layouts. - Michael Mayo; Maisa Daoud (PeerJ Prepr., 2017)

A Novel Synthetic Over-Sampling Technique for Imbalanced Classification of Gene Expressions Using Autoencoders and Swarm Optimization. - Maisa Daoud; Michael Mayo (Australasian Conference on Artificial Intelligence, 2018)

Neural Networks for Predicting the Output of wind flow Simulations Over Complex Topographies. - Michael Mayo; Sarah Wakes; Chris Anderson (ICBK, 2018)

Shock Absorbing Exoskeleton for Vertical Mobility System: Concept and Feasibility Study. - Jun Ueda; Melih Turkseven; Euisun Kim; Quincy Lowery; Courtland Bivens; Michael Mayo (IROS, 2018)

Using Swarm Optimization To Enhance Autoencoders Images. - Maisa Daoud; Michael Mayo (CoRR, 2018)

A survey of automatic de-identification of longitudinal clinical narratives. - Vithya Yogarajan; Michael Mayo; Bernhard Pfahringer (CoRR, 2018)

Improving the Robustness of the Glycemic Variability Percentage Metric to Sensor Dropouts in Continuous Glucose Monitor Data. - Michael Mayo (ACIIDS (2), 2019)

A Nearest Neighbour-Based Analysis to Identify Patients from Continuous Glucose Monitor Data. - Michael Mayo; Vithya Yogarajan (ACIIDS (2), 2019)

RBFA: Radial Basis Function Autoencoders. - Maisa Daoud; Michael Mayo; Sally Jo Cunningham (CEC, 2019)

Data Normalisation using Differential Evolution and Aggregated Logistic Functions. - Michael Mayo; Maisa Daoud (CEC, 2019)

Maintaining SLOs of Cloud-Native Applications Via Self-Adaptive Resource Sharing. - Vladimir Podolskiy; Michael Mayo; Abigail M. Y. Koay; Michael Gerndt; Panos Patros (SASO, 2019)

A survey of neural network-based cancer prediction models from microarray data. - Maisa Daoud; Michael Mayo (Artif. Intell. Medicine, 2019)

Automatic end-to-end De-identification: Is high accuracy the only metric? - Vithya Yogarajan; Bernhard Pfahringer; Michael Mayo (CoRR, 2019)

Metaheuristics for Discovering Favourable Continuous Intravenous Insulin Rate Protocols from Historical Patient Data. - Hongyu Wang 008; Lynne Chepulis; Ryan G. Paul; Michael Mayo (ACIIDS (1), 2020)

Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words for Predicting Medical Codes. - Vithya Yogarajan; Henry Gouk; Tony Smith; Michael Mayo; Bernhard Pfahringer (ACIIDS (1), 2020)

A Comparison of Machine Learning Methods for Cross-Domain Few-Shot Learning. - Hongyu Wang 008; Henry Gouk; Eibe Frank; Bernhard Pfahringer; Michael Mayo (Australasian Conference on Artificial Intelligence, 2020)

Interpretable Machine Learning with Bitonic Generalized Additive Models and Automatic Feature Construction. - Noëlie Cherrier; Michael Mayo; Jean-Philippe Poli; Maxime Defurne; Franck Sabatié (DS, 2020)

In Silico Comparison of Continuous Glucose Monitor Failure Mode Strategies for an Artificial Pancreas. - Yunjie Lisa Lu; Abigail M. Y. Koay; Michael Mayo (KDH@ECAI, 2020)

Neural Multi-class Classification Approach to Blood Glucose Level Forecasting with Prediction Uncertainty Visualisation. - Michael Mayo; Tomas Koutny (KDH@ECAI, 2020)

Predicting Diabetes Mellitus and its Complications through a Graph-Based Risk Scoring System. - Madurapperumage A. Erandathi; William Y. C. Wang; Michael Mayo (ICMHI, 2020)

Improving Naive Bayes for Regression with Optimized Artificial Surrogate Data. - Michael Mayo; Eibe Frank (Appl. Artif. Intell., 2020)

A review of Automatic end-to-end De-Identification: Is High Accuracy the Only Metric? - Vithya Yogarajan; Bernhard Pfahringer; Michael Mayo (Appl. Artif. Intell., 2020)

A Systematic Review on Extracting Predictors for Forecasting Complications of Diabetes Mellitus. - Anuradha Madurapperumage; William Yu Chung Wang; Michael Mayo (ICMHI, 2021)

Comparison Of Pretrained Feature Extractors For Glaucoma Detection. - Hansi Gunasinghe; James McKelvie; Abigail M. Y. Koay; Michael Mayo (ISBI, 2021)

Interpretable Deep Learning for Surgical Tool Management. - Mark Rodrigues; Michael Mayo; Panos Patros (iMIMIC/TDA4MedicalData@MICCAI, 2021)

Metaheuristic Optimization of Insulin Infusion Protocols Using Historical Data with Validation Using a Patient Simulator. - Hongyu Wang 008; Lynne Chepulis; Ryan G. Paul; Michael Mayo (Vietnam. J. Comput. Sci., 2021)

Adaptive Edge-Cloud Environments for Rural AI. - Osama Almurshed; Panos Patros; Victoria Huang 001; Michael Mayo; Melanie Ooi; Ryan Chard; Kyle Chard; Omer F. Rana; Harshaan Nagra; Matt Baughman; Ian T. Foster (SCC, 2022)

Domain Generalisation for Glaucoma Detection in Retinal Images from Unseen Fundus Cameras. - Hansi Gunasinghe; James McKelvie; Abigail M. Y. Koay; Michael Mayo (ACIIDS (2), 2022)

Clinically-relevant Summarisation of Cataract Surgery Videos Using Deep Learning. - Jesse Whitten; James McKelvie; Michael Mayo (ACIIDS (Companion), 2022)

Evaluation of Deep Learning Techniques on a Novel Hierarchical Surgical Tool Dataset. - Mark Rodrigues; Michael Mayo; Panos Patros (AI, 2022)

Experiments in Cross-domain Few-shot Learning for Image Classification: Extended Abstract. - Hongyu Wang 008; Huon Fraser; Henry Gouk; Eibe Frank; Bernhard Pfahringer; Michael Mayo; Geoff Holmes 001 (Meta-Knowledge Transfer @ ECML/PKDD, 2022)

Prevalence of sociodemographic factors in a cohort of diabetes mellitus: a retrospective study. - Madurapperumage A. Erandathi; William Yu Chung Wang; Michael Mayo; Ibrahim Shafiu (ICMHI, 2022)

Alzheimer's Disease Detection via a Surrogate Brain Age Prediction Task using 3D Convolutional Neural Networks. - Chen Zheng; Bernhard Pfahringer; Michael Mayo (IJCNN, 2022)

Assessing Vulnerability from Its Description. - Zijing Zhang; Vimal Kumar 001; Michael Mayo; Albert Bifet (UbiSec, 2022)

Predicting glucose level with an adapted branch predictor. - Tomas Koutny; Michael Mayo (Comput. Biol. Medicine, 2022)

Cross-domain Few-shot Meta-learning Using Stacking. - Hongyu Wang 008; Eibe Frank; Bernhard Pfahringer; Michael Mayo; Geoffrey Holmes 001 (CoRR, 2022)

Surgical Tool Datasets for Machine Learning Research: A Survey. - Mark Rodrigues; Michael Mayo; Panos Patros (Int. J. Comput. Vis., 2022)

Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients. - Victoria Huang 001; Shaleeza Sohail; Michael Mayo; Tania Lorido-Botran; Mark Rodrigues; Chris Anderson; Melanie Ooi (CLOUD, 2023)

Rural AI: Serverless-Powered Federated Learning for Remote Applications. - Panos Patros; Melanie Ooi; Victoria Huang 001; Michael Mayo; Chris Anderson; Stephen Burroughs; Matt Baughman; Osama Almurshed; Omer F. Rana; Ryan Chard; Kyle Chard; Ian T. Foster (IEEE Internet Comput., 2023)

Feature extractor stacking for cross-domain few-shot learning. - Hongyu Wang 008; Eibe Frank; Bernhard Pfahringer; Michael Mayo; Geoff Holmes 001 (Mach. Learn., 2024)