Santu Rana

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Svetha VenkateshProfessor of Computer Science, Deakin UniversityVerified email at deakin.edu.au
Sunil GuptaAssociate Professor, Deakin UniversityVerified email at deakin.edu.au
Dinh PhungProfessor, Monash UniversityVerified email at monash.edu
Cheng LiNational University of Singapore, SingaporeVerified email at comp.nus.edu.sg
Vu NguyenUniversity of OxfordVerified email at robots.ox.ac.uk
Alistair ShiltonApplied Artificial Intelligence Institute (A²I²), Deakin UniversityVerified email at deakin.edu.au
Truyen TranAssociate Professor, Applied AI Institute, Deakin UniversityVerified email at deakin.edu.au
Wei LuoDeakin UniversityVerified email at deakin.edu.au
Alessandra SuttiDeakin University, Institute for Frontier MaterialsVerified email at deakin.edu.au
Tinu Theckel JoyIBM ResearchVerified email at ibm.com
David Rubín de Celis LealAssociate Research FellowVerified email at deakin.edu.au
Michael BerkDeakin UniversityVerified email at deakin.edu.au
Thin NguyenSenior Research Fellow, Deakin University, AustraliaVerified email at deakin.edu.au
Pratibha VellankiDeakin University, AustraliaVerified email at research.deakin.edu.au
Stewart GreenhillDeakin UniversityVerified email at deakin.edu.au
Wanquan Liu,Curtin UniversityVerified email at curtin.edu.au
Majid AbdolshahAssociate research fellow @ Applied Artificial Intelligence Institute (A²I²), Deakin UniversityVerified email at deakin.edu.au
Steven AllenderGlobal Obesity Centre (GLOBE), WHO Collaborating Centre for Obesity Prevention, Deakin UniversityVerified email at deakin.edu.au
Anil RamachandranPhd Student, Deakin UniversityVerified email at deakin.edu.au
HARIPRIYA HARIKUMARResearch, Deakin UniversityVerified email at deakin.edu.au

Santu Rana

Associate Professor of Computer Science, Deakin University

Verified email at deakin.edu.au - Homepage

Machine Learning Bayesian Optimisation Health Informatics


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Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view W Luo, D Phung, T Tran, S Gupta, S Rana, C Karmakar, A Shilton, ... Journal of medical Internet research 18 (12), e323, 2016	228	2016
High dimensional bayesian optimization using dropout C Li, S Gupta, S Rana, V Nguyen, S Venkatesh, A Shilton 26th International Joint Conference on Artificial Intelligence, 2096--2102, 2017	61	2017
Rapid Bayesian optimisation for synthesis of short polymer fiber materials C Li, DR de Celis Leal, S Rana, S Gupta, A Sutti, S Greenhill, T Slezak, ... Scientific reports 7 (1), 1-10, 2017	59	2017
High dimensional Bayesian optimization with elastic Gaussian process S Rana, C Li, S Gupta, V Nguyen, S Venkatesh International conference on machine learning, 2883-2891, 2017	52	2017
Differentially private random forest with high utility S Rana, SK Gupta, S Venkatesh 2015 IEEE International Conference on Data Mining, 955-960, 2015	50	2015
A framework for feature extraction from hospital medical data with applications in risk prediction T Tran, W Luo, D Phung, S Gupta, S Rana, RL Kennedy, A Larkins, ... BMC bioinformatics 15 (1), 425, 2014	41*	2014
Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data S Rana, T Tran, W Luo, D Phung, RL Kennedy, S Venkatesh Australian Health Review 38 (4), 377-382, 2014	41	2014
Hyperparameter tuning for big data using Bayesian optimisation TT Joy, S Rana, S Gupta, S Venkatesh 2016 23rd International Conference on Pattern Recognition (ICPR), 2574-2579, 2016	26*	2016
Regret for expected improvement over the best-observed value and stopping condition V Nguyen, S Gupta, S Rana, C Li, S Venkatesh Asian Conference on Machine Learning, 279-294, 2017	25	2017
Budgeted batch Bayesian optimization V Nguyen, S Rana, SK Gupta, C Li, S Venkatesh 2016 IEEE 16th International Conference on Data Mining (ICDM), 1107-1112, 2016	24	2016
A unified tensor framework for face recognition S Rana, W Liu, M Lazarescu, S Venkatesh Pattern Recognition 42 (11), 2850-2862, 2009	24	2009
Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset W Luo, T Nguyen, M Nichols, T Tran, S Rana, S Gupta, D Phung, ... PloS one 10 (5), e0125602, 2015	23	2015
Recognising faces in unseen modes: a tensor based approach S Rana, W Liu, M Lazarescu, S Venkatesh 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008	23	2008
Regret bounds for transfer learning in Bayesian optimisation A Shilton, S Gupta, S Rana, S Venkatesh Artificial Intelligence and Statistics, 307-315, 2017	17	2017
A flexible transfer learning framework for Bayesian optimization with convergence guarantee TT Joy, S Rana, S Gupta, S Venkatesh Expert Systems with Applications 115, 656-672, 2019	16	2019
Web search activity data accurately predict population chronic disease risk in the USA T Nguyen, T Tran, W Luo, S Gupta, S Rana, D Phung, M Nichols, L Millar, ... J Epidemiol Community Health 69 (7), 693-699, 2015	16	2015
A Bayesian nonparametric approach for multi-label classification V Nguyen, S Gupta, S Rana, C Li, S Venkatesh Asian conference on machine learning, 254-269, 2016	14	2016
Large-scale statistical modeling of motion patterns: a Bayesian nonparametric approach S Rana, D Phung, S Pham, S Venkatesh Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and …, 2012	14	2012
Accelerating experimental design by incorporating experimenter hunches C Li, S Rana, S Gupta, V Nguyen, S Venkatesh, A Sutti, D Rubin, T Slezak, ... IEEE International Conference on Data Mining (ICDM), 257--266, 2018	13	2018
Flexible transfer learning framework for Bayesian optimisation TT Joy, S Rana, SK Gupta, S Venkatesh Pacific-Asia Conference on Knowledge Discovery and Data Mining, 102-114, 2016	13	2016

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