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2020 – today
- 2024
[j13]Hongyu Wang
, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoff Holmes:
Feature extractor stacking for cross-domain few-shot learning. Mach. Learn. 113(1): 121-158 (2024)- 2023
- [j12]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): 28-34 (2023) - [c54]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: 1-3 - [i7]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 abs/2305.09856 (2023) - 2022
- [j11]Tomas Koutny, Michael Mayo:
Predicting glucose level with an adapted branch predictor. Comput. Biol. Medicine 145: 105388 (2022) - [j10]Mark Rodrigues, Michael Mayo, Panos Patros:
Surgical Tool Datasets for Machine Learning Research: A Survey. Int. J. Comput. Vis. 130(9): 2222-2248 (2022) - [c53]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: 74-83 - [c52]Hansi Gunasinghe, James McKelvie, Abigail M. Y. Koay, Michael Mayo:
Domain Generalisation for Glaucoma Detection in Retinal Images from Unseen Fundus Cameras. ACIIDS (2) 2022: 421-433 - [c51]Jesse Whitten, James McKelvie, Michael Mayo:
Clinically-relevant Summarisation of Cataract Surgery Videos Using Deep Learning. ACIIDS (Companion) 2022: 711-723 - [c50]Mark Rodrigues, Michael Mayo, Panos Patros:
Evaluation of Deep Learning Techniques on a Novel Hierarchical Surgical Tool Dataset. AI 2022: 169-180 - [c49]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: 81-83 - [c48]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: 193-197 - [c47]Chen Zheng, Bernhard Pfahringer, Michael Mayo:
Alzheimer's Disease Detection via a Surrogate Brain Age Prediction Task using 3D Convolutional Neural Networks. IJCNN 2022: 1-8 - [c46]Zijing Zhang, Vimal Kumar, Michael Mayo, Albert Bifet:
Assessing Vulnerability from Its Description. UbiSec 2022: 129-143 - [i6]Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoffrey Holmes:
Cross-domain Few-shot Meta-learning Using Stacking. CoRR abs/2205.05831 (2022) - 2021
- [j9]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): 263-290 (2021) - [c45]Anuradha Madurapperumage, William Yu Chung Wang, Michael Mayo:
A Systematic Review on Extracting Predictors for Forecasting Complications of Diabetes Mellitus. ICMHI 2021: 327-330 - [c44]Hansi Gunasinghe, James McKelvie, Abigail M. Y. Koay, Michael Mayo:
Comparison Of Pretrained Feature Extractors For Glaucoma Detection. ISBI 2021: 390-394 - [c43]Mark Rodrigues, Michael Mayo, Panos Patros:
Interpretable Deep Learning for Surgical Tool Management. iMIMIC/TDA4MedicalData@MICCAI 2021: 3-12 - 2020
- [j8]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): 251-269 (2020) - [j7]Michael Mayo, Eibe Frank:
Improving Naive Bayes for Regression with Optimized Artificial Surrogate Data. Appl. Artif. Intell. 34(6): 484-514 (2020) - [c42]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: 97-108 - [c41]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: 157-169 - [c40]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: 445-457 - [c39]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: 386-402 - [c38]Yunjie Lisa Lu, Abigail M. Y. Koay, Michael Mayo:
In Silico Comparison of Continuous Glucose Monitor Failure Mode Strategies for an Artificial Pancreas. KDH@ECAI 2020: 53-57 - [c37]Michael Mayo, Tomas Koutny:
Neural Multi-class Classification Approach to Blood Glucose Level Forecasting with Prediction Uncertainty Visualisation. KDH@ECAI 2020: 80-84 - [c36]Madurapperumage A. Erandathi, William Y. C. Wang, Michael Mayo:
Predicting Diabetes Mellitus and its Complications through a Graph-Based Risk Scoring System. ICMHI 2020: 1-7
2010 – 2019
- 2019
- [j6]Maisa Daoud, Michael Mayo:
A survey of neural network-based cancer prediction models from microarray data. Artif. Intell. Medicine 97: 204-214 (2019) - [c35]Michael Mayo, Vithya Yogarajan:
A Nearest Neighbour-Based Analysis to Identify Patients from Continuous Glucose Monitor Data. ACIIDS (2) 2019: 349-360 - [c34]Michael Mayo:
Improving the Robustness of the Glycemic Variability Percentage Metric to Sensor Dropouts in Continuous Glucose Monitor Data. ACIIDS (2) 2019: 373-384 - [c33]Michael Mayo, Maisa Daoud:
Data Normalisation using Differential Evolution and Aggregated Logistic Functions. CEC 2019: 920-927 - [c32]Maisa Daoud, Michael Mayo, Sally Jo Cunningham:
RBFA: Radial Basis Function Autoencoders. CEC 2019: 2966-2973 - [c31]Vladimir Podolskiy, Michael Mayo, Abigail M. Y. Koay, Michael Gerndt, Panos Patros:
Maintaining SLOs of Cloud-Native Applications Via Self-Adaptive Resource Sharing. SASO 2019: 72-81 - [i5]Vithya Yogarajan, Bernhard Pfahringer, Michael Mayo:
Automatic end-to-end De-identification: Is high accuracy the only metric? CoRR abs/1901.10583 (2019) - 2018
- [c30]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: 603-615 - [c29]Michael Mayo, Sarah Wakes, Chris Anderson:
Neural Networks for Predicting the Output of wind flow Simulations Over Complex Topographies. ICBK 2018: 184-191 - [c28]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: 3342-3349 - [i4]Maisa Daoud, Michael Mayo:
Using Swarm Optimization To Enhance Autoencoders Images. CoRR abs/1807.03346 (2018) - [i3]Vithya Yogarajan, Michael Mayo, Bernhard Pfahringer:
A survey of automatic de-identification of longitudinal clinical narratives. CoRR abs/1810.06765 (2018) - 2017
- [j5]Michael Mayo, Maisa Daoud:
Aesthetic Local Search of Wind Farm Layouts. Inf. 8(2): 39 (2017) - [c27]Michael Mayo, Sean Goltz:
Constructing Document Vectors Using Kernel Density Estimates. MDAI 2017: 183-194 - [c26]Brett Wilson, Sarah Wakes, Michael Mayo:
Surrogate modeling a computational fluid dynamics-based wind turbine wake simulation using machine learning. SSCI 2017: 1-8 - [i2]Michael Mayo, Eibe Frank:
Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data. CoRR abs/1707.04943 (2017) - [i1]Michael Mayo, Maisa Daoud:
Aesthetic local search of wind farm layouts. PeerJ Prepr. 5: e2864 (2017) - 2016
- [c25]Michael Mayo, Chen Zheng:
BlockCopy-based operators for evolving efficient wind farm layouts. CEC 2016: 1085-1092 - [c24]Michael Mayo, Maisa Daoud, Chen Zheng:
Proceedings in Adaptation, Learning and Optimization. IES 2016: 277-289 - [c23]Michael Mayo, Sara Omranian:
Towards a New Evolutionary Subsampling Technique for Heuristic Optimisation of Load Disaggregators. PAKDD Workshops 2016: 3-14 - [c22]Michael Mayo, Albert Bifet:
Deferral classification of evolving temporal dependent data streams. SAC 2016: 952-954 - 2015
- [c21]Eibe Frank, Michael Mayo, Stefan Kramer:
Alternating model trees. SAC 2015: 871-878 - [c20]Michael Mayo, Maisa Daoud:
An Adaptive Model-Based Mutation Operator for the Wind Farm Layout Optimisation Problem. SMC 2015: 671-676 - 2014
- [c19]Michael Mayo, Quan Sun:
Evolving artificial datasets to improve interpretable classifiers. IEEE Congress on Evolutionary Computation 2014: 2367-2374 - [e1]Michael J. Cree, Lee V. Streeter, John A. Perrone, Michael Mayo, Anthony M. Blake:
Proceedings of the 29th International Conference on Image and Vision Computing New Zealand, IVCNZ 2014, Hamilton, New Zealand, November 19-21, 2014. ACM 2014, ISBN 978-1-4503-3184-5 [contents] - 2013
- [c18]Michael Mayo:
Identifying Market Price Levels Using Differential Evolution. EvoApplications 2013: 203-212 - [c17]Quan Sun, Bernhard Pfahringer, Michael Mayo:
Towards a Framework for Designing Full Model Selection and Optimization Systems. MCS 2013: 259-270 - [c16]Michael Mayo, Simon A. Spacey:
Predicting Regression Test Failures Using Genetic Algorithm-Selected Dynamic Performance Analysis Metrics. SSBSE 2013: 158-171 - 2012
- [c15]Michael Mayo:
Cartesian Genetic Programming for Trading: A Preliminary Investigation. AusDM 2012: 149-156 - [c14]Michael Mayo:
Evolutionary Data Selection for Enhancing Models of Intraday Forex Time Series. EvoApplications 2012: 184-193 - [c13]Quan Sun, Bernhard Pfahringer, Michael Mayo:
Full model selection in the space of data mining operators. GECCO (Companion) 2012: 1503-1504 - 2011
- [j4]Michael Mayo, Lorenzo Beretta:
Modelling epistasis in genetic disease using Petri nets, evolutionary computation and frequent itemset mining. Expert Syst. Appl. 38(4): 4006-4013 (2011) - [c12]Michael Mayo:
Hybridizing Data Stream Mining and Technical Indicators in Automated Trading Systems. MDAI 2011: 79-90 - 2010
- [j3]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): O3 (2010) - [c11]Michael Mayo, Lorenzo Beretta:
Evolving Concurrent Petri Net Models of Epistasis. ACIIDS (2) 2010: 166-175
2000 – 2009
- 2009
- [c10]Michael Mayo, Edmond Zhang:
3D Face Recognition Using Multiview Keypoint Matching. AVSS 2009: 290-295 - [c9]Edmond Zhang, Michael Mayo:
SIFTing the Relevant from the Irrelevant: Automatically Detecting Objects in Training Images. DICTA 2009: 317-324 - 2007
- [j2]Michael Mayo, Anna T. Watson:
Automatic species identification of live moths. Knowl. Based Syst. 20(2): 195-202 (2007) - [c8]Michael Mayo:
Random Convolution Ensembles. PCM 2007: 216-225 - 2006
- [c7]Michael Mayo, Anna T. Watson:
Automatic Species Identification of Live Moths. SGAI Conf. (Applications) 2006: 58-71 - 2005
- [c6]Michael Mayo:
Bayesian Sequence Learning for Predicting Protein Cleavage Points. PAKDD 2005: 192-202 - 2004
- [c5]Margaret E. Jefferies, Wenrong Weng, Jesse T. Baker, Michael Mayo:
Using Context to Solve the Correspondence Problem in Simultaneous Localisation and Mapping. PRICAI 2004: 664-672 - [c4]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: 434-454 - 2002
- [j1]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): 243-279 (2002) - 2001
- [c3]Antonija Mitrovic, Michael Mayo, Pramuditha Suraweera, Brent Martin:
Constraint-Based Tutors: A Success Story. IEA/AIE 2001: 931-940 - 2000
- [c2]Michael Mayo, Antonija Mitrovic:
Using a Probabilistic Student Model to Control Problem Difficulty. Intelligent Tutoring Systems 2000: 524-533
1990 – 1999
- 1999
- [c1]Michael Mayo, Antonija Mitrovic:
Estimating Problem Value in an Intelligent Tutoring System Using Bayesian Networks. Australian Joint Conference on Artificial Intelligence 1999: 472-473
Coauthor Index
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