Michael Mayo

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 49

Top Topics

Diabetes Mellitus

Machine Learning

Kernel Density Estimates

Feature Extractors

Top Venues

Publications

Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoff Holmes
Feature extractor stacking for cross-domain few-shot learning. Mach. Learn. 113 (1) (2024)
Panos Patros, Melanie Ooi, Victoria Huang, Michael Mayo, Chris Anderson, Stephen Burroughs, Matt Baughman, Osama Almurshed, Omer F. Rana, Ryan Chard, Kyle Chard, Ian T. Foster
Rural AI: Serverless-Powered Federated Learning for Remote Applications. IEEE Internet Comput. 27 (2) (2023)
Victoria Huang, Shaleeza Sohail, Michael Mayo, Tania Lorido-Botran, Mark Rodrigues, Chris Anderson, Melanie Ooi
Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients. CoRR (2023)
Victoria Huang, Shaleeza Sohail, Michael Mayo, Tania Lorido-Botran, Mark Rodrigues, Chris Anderson, Melanie Ooi
Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients. CLOUD (2023)
Zijing Zhang, Vimal Kumar, Michael Mayo, Albert Bifet
Assessing Vulnerability from Its Description. UbiSec (2022)
Mark Rodrigues, Michael Mayo, Panos Patros
Surgical Tool Datasets for Machine Learning Research: A Survey. Int. J. Comput. Vis. 130 (9) (2022)
Hansi Gunasinghe, James McKelvie, Abigail Koay, Michael Mayo
Domain Generalisation for Glaucoma Detection in Retinal Images from Unseen Fundus Cameras. ACIIDS (2) (2022)
Madurapperumage A. Erandathi, William Yu Chung Wang, Michael Mayo, Ibrahim Shafiu
Prevalence of sociodemographic factors in a cohort of diabetes mellitus: a retrospective study. ICMHI (2022)
Chen Zheng, Bernhard Pfahringer, Michael Mayo
Alzheimer's Disease Detection via a Surrogate Brain Age Prediction Task using 3D Convolutional Neural Networks. IJCNN (2022)
Mark Rodrigues, Michael Mayo, Panos Patros
Evaluation of Deep Learning Techniques on a Novel Hierarchical Surgical Tool Dataset. AI (2022)
Hongyu Wang, Huon Fraser, Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoff Holmes
Experiments in Cross-domain Few-shot Learning for Image Classification: Extended Abstract. Meta-Knowledge Transfer @ ECML/PKDD (2022)
Osama Almurshed, Panos Patros, Victoria Huang, Michael Mayo, Melanie Ooi, Ryan Chard, Kyle Chard, Omer F. Rana, Harshaan Nagra, Matt Baughman, Ian T. Foster
Adaptive Edge-Cloud Environments for Rural AI. SCC (2022)
Jesse Whitten, James McKelvie, Michael Mayo
Clinically-relevant Summarisation of Cataract Surgery Videos Using Deep Learning. ACIIDS (Companion) (2022)
Tomas Koutny, Michael Mayo
Predicting glucose level with an adapted branch predictor. Comput. Biol. Medicine 145 (2022)
Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoffrey Holmes
Cross-domain Few-shot Meta-learning Using Stacking. CoRR (2022)
Mark Rodrigues, Michael Mayo, Panos Patros
Interpretable Deep Learning for Surgical Tool Management. iMIMIC/TDA4MedicalData@MICCAI (2021)
Hongyu Wang, Lynne Chepulis, Ryan G. Paul, Michael Mayo
Metaheuristic Optimization of Insulin Infusion Protocols Using Historical Data with Validation Using a Patient Simulator. Vietnam. J. Comput. Sci. 8 (2) (2021)
Anuradha Madurapperumage, William Yu Chung Wang, Michael Mayo
A Systematic Review on Extracting Predictors for Forecasting Complications of Diabetes Mellitus. ICMHI (2021)
Hansi Gunasinghe, James McKelvie, Abigail Koay, Michael Mayo
Comparison Of Pretrained Feature Extractors For Glaucoma Detection. ISBI (2021)
Madurapperumage A. Erandathi, William Y. C. Wang, Michael Mayo
Predicting Diabetes Mellitus and its Complications through a Graph-Based Risk Scoring System. ICMHI (2020)
Yunjie Lisa Lu, Abigail Koay, Michael Mayo
In Silico Comparison of Continuous Glucose Monitor Failure Mode Strategies for an Artificial Pancreas. KDH@ECAI (2020)
Michael Mayo, Tomas Koutny
Neural Multi-class Classification Approach to Blood Glucose Level Forecasting with Prediction Uncertainty Visualisation. KDH@ECAI (2020)
Michael Mayo, Eibe Frank
Improving Naive Bayes for Regression with Optimized Artificial Surrogate Data. Appl. Artif. Intell. 34 (6) (2020)
Vithya Yogarajan, Henry Gouk, Tony Smith, Michael Mayo, Bernhard Pfahringer
Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words for Predicting Medical Codes. ACIIDS (1) (2020)
Hongyu Wang, Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael Mayo
A Comparison of Machine Learning Methods for Cross-Domain Few-Shot Learning. Australasian Conference on Artificial Intelligence (2020)
Hongyu Wang, Lynne Chepulis, Ryan G. Paul, Michael Mayo
Metaheuristics for Discovering Favourable Continuous Intravenous Insulin Rate Protocols from Historical Patient Data. ACIIDS (1) (2020)
Noëlie Cherrier, Michael Mayo, Jean-Philippe Poli, Maxime Defurne, Franck Sabatié
Interpretable Machine Learning with Bitonic Generalized Additive Models and Automatic Feature Construction. DS (2020)
Vithya Yogarajan, Bernhard Pfahringer, Michael Mayo
A review of Automatic end-to-end De-Identification: Is High Accuracy the Only Metric? Appl. Artif. Intell. 34 (3) (2020)
Vladimir Podolskiy, Michael Mayo, Abigail Koay, Michael Gerndt, Panos Patros
Maintaining SLOs of Cloud-Native Applications Via Self-Adaptive Resource Sharing. SASO (2019)
Michael Mayo, Maisa Daoud
Data Normalisation using Differential Evolution and Aggregated Logistic Functions. CEC (2019)
Michael Mayo
Improving the Robustness of the Glycemic Variability Percentage Metric to Sensor Dropouts in Continuous Glucose Monitor Data. ACIIDS (2) (2019)
Michael Mayo, Vithya Yogarajan
A Nearest Neighbour-Based Analysis to Identify Patients from Continuous Glucose Monitor Data. ACIIDS (2) (2019)
Vithya Yogarajan, Bernhard Pfahringer, Michael Mayo
Automatic end-to-end De-identification: Is high accuracy the only metric? CoRR (2019)
Maisa Daoud, Michael Mayo
A survey of neural network-based cancer prediction models from microarray data. Artif. Intell. Medicine 97 (2019)
Maisa Daoud, Michael Mayo, Sally Jo Cunningham
RBFA: Radial Basis Function Autoencoders. CEC (2019)
Michael Mayo, Sarah Wakes, Chris Anderson
Neural Networks for Predicting the Output of wind flow Simulations Over Complex Topographies. ICBK (2018)
Maisa Daoud, Michael Mayo
A Novel Synthetic Over-Sampling Technique for Imbalanced Classification of Gene Expressions Using Autoencoders and Swarm Optimization. Australasian Conference on Artificial Intelligence (2018)
Maisa Daoud, Michael Mayo
Using Swarm Optimization To Enhance Autoencoders Images. CoRR (2018)
Jun Ueda, Melih Turkseven, Euisun Kim, Quincy Lowery, Courtland Bivens, Michael Mayo
Shock Absorbing Exoskeleton for Vertical Mobility System: Concept and Feasibility Study. IROS (2018)
Vithya Yogarajan, Michael Mayo, Bernhard Pfahringer
A survey of automatic de-identification of longitudinal clinical narratives. CoRR (2018)
Michael Mayo, Maisa Daoud
Aesthetic local search of wind farm layouts. PeerJ Prepr. 5 (2017)
Michael Mayo, Maisa Daoud
Aesthetic Local Search of Wind Farm Layouts. Inf. 8 (2) (2017)
Brett Wilson, Sarah Wakes, Michael Mayo
Surrogate modeling a computational fluid dynamics-based wind turbine wake simulation using machine learning. SSCI (2017)
Michael Mayo, Sean Goltz
Constructing Document Vectors Using Kernel Density Estimates. MDAI (2017)
Michael Mayo, Eibe Frank
Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data. CoRR (2017)
Michael Mayo, Sara Omranian
Towards a New Evolutionary Subsampling Technique for Heuristic Optimisation of Load Disaggregators. PAKDD Workshops (2016)
Michael Mayo, Albert Bifet
Deferral classification of evolving temporal dependent data streams. SAC (2016)
Michael Mayo, Chen Zheng
BlockCopy-based operators for evolving efficient wind farm layouts. CEC (2016)
Michael Mayo, Maisa Daoud, Chen Zheng
Proceedings in Adaptation, Learning and Optimization. IES (2016)
Michael Mayo, Maisa Daoud
An Adaptive Model-Based Mutation Operator for the Wind Farm Layout Optimisation Problem. SMC (2015)
Eibe Frank, Michael Mayo, Stefan Kramer
Alternating model trees. SAC (2015)
Michael Mayo, Quan Sun
Evolving artificial datasets to improve interpretable classifiers. IEEE Congress on Evolutionary Computation (2014)
Michael Mayo, Simon A. Spacey
Predicting Regression Test Failures Using Genetic Algorithm-Selected Dynamic Performance Analysis Metrics. SSBSE (2013)
Nicholas Barnes, Lynette A. Hunt, Michael Mayo
Classification Models in Intensive Care Outcome Prediction - Can We Improve on Current Models? AIH+CARE@AUS-AI (2013)
Michael Mayo
Identifying Market Price Levels Using Differential Evolution. EvoApplications (2013)
Quan Sun, Bernhard Pfahringer, Michael Mayo
Towards a Framework for Designing Full Model Selection and Optimization Systems. MCS (2013)
Michael Mayo
Cartesian Genetic Programming for Trading: A Preliminary Investigation. AusDM (2012)
Quan Sun, Bernhard Pfahringer, Michael Mayo
Full model selection in the space of data mining operators. GECCO (Companion) (2012)
Michael Mayo
Evolutionary Data Selection for Enhancing Models of Intraday Forex Time Series. EvoApplications (2012)
Michael Mayo, Lorenzo Beretta
Modelling epistasis in genetic disease using Petri nets, evolutionary computation and frequent itemset mining. Expert Syst. Appl. 38 (4) (2011)
Michael Mayo
Hybridizing Data Stream Mining and Technical Indicators in Automated Trading Systems. MDAI (2011)
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, John Yee, Montgomery Martin, Michael Mayo, Nataliya Melnyk, Ryan D. Morin, Trevor J. Pugh, Tesa Severson, Sohrab P. Shah, Margaret Sutcliffe, Angela Tam, Jefferson Terry, Nina Thiessen, Thomas Thomson, Richard Varhol, Thomas Zeng, Yongjun Zhao, Richard A. Moore, David G. Huntsman, Inanç Birol, Martin Hirst, Robert A. Holt, Marco A. Marra
Genomic analysis of a rare human tumor. BMC Bioinform. 11 (S-4) (2010)
Michael Mayo, Lorenzo Beretta
Evolving Concurrent Petri Net Models of Epistasis. ACIIDS (2) (2010)
Edmond Zhang, Michael Mayo
SIFTing the Relevant from the Irrelevant: Automatically Detecting Objects in Training Images. DICTA (2009)
Michael Mayo, Edmond Zhang
3D Face Recognition Using Multiview Keypoint Matching. AVSS (2009)
Michael Mayo
Random Convolution Ensembles. PCM (2007)
Michael Mayo, Anna T. Watson
Automatic species identification of live moths. Knowl. Based Syst. 20 (2) (2007)
Michael Mayo, Anna T. Watson
Automatic Species Identification of Live Moths. SGAI Conf. (Applications) (2006)
Michael Mayo
Bayesian Sequence Learning for Predicting Protein Cleavage Points. PAKDD (2005)
Margaret E. Jefferies, Michael J. Cree, Michael Mayo, Jesse T. Baker
Using 2D and 3D Landmarks to Solve the Correspondence Problem in Cognitive Robot Mapping. Spatial Cognition (2004)
Margaret E. Jefferies, Wenrong Weng, Jesse T. Baker, Michael Mayo
Using Context to Solve the Correspondence Problem in Simultaneous Localisation and Mapping. PRICAI (2004)
Antonija Mitrovic, Brent Martin, Michael Mayo
Using Evaluation to Shape ITS Design: Results and Experiences with SQL-Tutor. User Model. User Adapt. Interact. 12 (2-3) (2002)
Antonija Mitrovic, Michael Mayo, Pramuditha Suraweera, Brent Martin
Constraint-Based Tutors: A Success Story. IEA/AIE (2001)
Michael Mayo, Antonija Mitrovic
Using a Probabilistic Student Model to Control Problem Difficulty. Intelligent Tutoring Systems (2000)
Michael Mayo, Antonija Mitrovic
Estimating Problem Value in an Intelligent Tutoring System Using Bayesian Networks. Australian Joint Conference on Artificial Intelligence (1999)