Dr Alistair Shilton

STAFF PROFILE

Position

Research Fellow

Faculty

Applied Artificial Intel Inst

Department

A2I2P

Campus

Geelong Waurn Ponds Campus

Publications

Filter by

2020

Multiclass anomaly detector: The cs++ support vector machine

A Shilton, S Rajasegarar, M Palaniswami

(2020), Vol. 21, pp. 1-39, Journal of Machine Learning Research, Cambridge, Ma., C1

journal article

Accelerated Bayesian Optimization through Weight-Prior Tuning

Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Laurence Park, Cheng Li, Svetha Venkatesh, Thomas Dorin, Alessandra Sutti, David Rubin, Teo Slezak, Alireza Vahid, Murray Height

(2020), Vol. 108, pp. 1-10, AISTATS 2020 : Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, Online from Palermo, Italy, E1

conference
2019

Multi-objective Bayesian optimisation with preferences over objectives

M Abdolshah, A Shilton, S Rana, S Gupta, S Venkatesh

(2019), Vol. 32, pp. 1-11, NeurIPS 2019 : Proceedings of the 33rd Conference on Neural Information Processing Systems, Vancouver, British Columbia, E1

conference
2018

Exploiting strategy-space diversity for batch Bayesian optimization

S Gupta, Alistair Shilton, Santu Rana, Svetha Venkatesh

(2018), Vol. 84, pp. 538-547, AISTATS 2018 : Proceedings of the International Conference on Artificial Intelligence and Statistics, Playa Blanca, Lanzarote, Canary Islands, E1

conference

Multi-target optimisation via Bayesian optimisation and linear programming

A Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh

(2018), UAI 2018: Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, Monterey, Calif., E1

conference

Expected hypervolume improvement with constraints

M Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, svetha Venkatesh

(2018), pp. 3238-3243, ICPR 2018 : Proceedings of the 24th International Conference on Pattern Recognition, Beijing, China, E1

conference
2017

Regret bounds for transfer learning in Bayesian optimisation

A Shilton, S Gupta, S Rana, S Venkatesh

(2017), Vol. 54, pp. 1-9, AISTATS 2017 : Machine Learning Research : Proceedings of the 20th Artificial Intelligence and Statistics International Conference, Fort Lauderdale, Florida, E1

conference

High dimensional bayesian optimization using dropout

C Li, S gupta, S Rana, V Nguyen, S Venkatesh, A Shilton

(2017), pp. 2096-2102, IJCAI 2017 : Proceedings of the 26th International Joint Confrerence on Artificial Intelligence, Melbourne, Victoria, E1

conference
2016

Guidelines for developing and reporting machine learning predictive models in biomedical research : a multidisciplinary view

W Luo, Q Phung, T Tran, S Gupta, S Rana, C Karmakar, A Shilton, J Yearwood, N Dimitrova, T Ho, S Venkatesh, M Berk

(2016), Vol. 18, pp. 1-10, Journal of medical internet research, Toronto, Ont., C1

journal article
2015

DP1SVM: a dynamic planar one-class support vector machine for Internet of Things environment

A Shilton, S Rajasegarar, C Leckie, M Palaniswami

(2015), pp. 1-6, RIoT 2015 : Proceedings of the 2015 International Conference on Recent Advances in Internet of Things, Singapore, Singapore, E1-1

conference
2013

Combined multiclass classification and anomaly detection for large-scale wireless sensor networks

A Shilton, S Rajasegarar, M Palaniswami

(2013), pp. 491-496, IEEE ISSNIP 2013 : Proceedings of the 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Vic., E1-1

conference

Autonomous detection of different walking tasks using end point foot trajectory vertical displacement data

B Santhiranayagam, D Lai, A Shilton, R Begg, M Palaniswami

(2013), pp. 509-514, IEEE ISSNIP 2013 : Proceedings of the 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Vic., E1-1

conference
2012

The challenges of monitoring physical activity in children with wearable sensor technologies

G Pendhakar, D Lai, A Shilton, R Polman

(2012), pp. 221-246, Healthcare sensor networks: challenges toward practical implementation, Boca Raton, Fla., B1-1

book chapter

A note on octonionic support vector regression

Alistair Shilton, Daniel Lai, Braveena Santhiranayagam, M Palaniswami

(2012), Vol. 42, pp. 950-955, IEEE transactions on systems, man and cybernetics - part B: cybernetics, Piscataway, N.J., C1-1

journal article

Fast supersymmetry phenomenology at the Large Hadron Collider using machine learning techniques

A Buckley, A Shilton, M White

(2012), Vol. 183, pp. 960-970, Computer physics communications, Amsterdam, The Netherlands, C1-1

journal article

Automatic detection of different walking conditions using inertial sensor data

B Santhiranayagam, D Lai, C Jiang, A Shilton, R Begg

(2012), Proceedings of the International Joint Conference on Neural Networks, Brisbane, QLD, E1-1

conference

The conic-segmentation support vector machine - A target space method for multiclass classification

Alistair Shilton, Daniel Lai, M Palaniswami

(2012), IJCNN 2012 : Proceedings of the International Joint Conference on Neural Networks, Brisbane, Queensland, E1-1

conference
2011

Regression models for estimating gait parameters using inertial sensors

B Santhiranayagam, D Lai, A Shilton, R Begg, M Palaniswami

(2011), pp. 46-51, ISSNIP 2011 : Proceedings of the 2011 7th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Adelaide, SA, E1-1

conference
2010

A division algebraic framework for multidimensional support vector regression

A Shilton, D Lai, M Palaniswami

(2010), Vol. 40, pp. 517-528, IEEE transactions on systems, man, and cybernetics, part B: cybernetics, Piscataway, N.J., C1-1

journal article

On the feasibility of learning to predict minimum toe clearance under different walking speeds

Daniel Lai, A Shilton, R Begg

(2010), Vol. 2010, pp. 4890-4893, EMBC'10 : Merging medical humanism and technology : Proceedings of the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, Argentina, E1-1

conference
2009

Kernel Methods in Protein Structure Prediction

J Gubbi, A Shilton, M Palaniswami

(2009), pp. 209-228, Machine Learning in Bioinformatics, London, Eng., B1-1

book chapter

A machine learning approach to k-step look-ahead prediction of gait variables from acceleration data

D Lai, A Shilton, E Charry, R Begg, M Palaniswami

(2009), pp. 384-387, Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, Minnesota, E1-1

conference
2007

Real value solvent accessibility prediction using adaptive support vector regression

J Gubbi, A Shilton, M Palaniswami, M Parker

(2007), pp. 395-401, CIBCB 2007 : Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, Honolulu, Hawaii, E1-1

conference

Iterative fuzzy support vector machine classification

A Shilton, D Lai

(2007), pp. 1-6, FUZZ-IEEE : Proceedings of the 2007 IEEE International Conference on Fuzzy Systems, London, Eng., E1-1

conference

Quaternionic and complex-valued support vector regression for equalization and function approximation

A Shilton, D Lai

(2007), pp. 1-6, IJCNN 2007 : Proceedings of the IEEE 2007 International Conference on Neural Networks, Orlando, Florida, E1-1

conference

Detecting selective forwarding attacks in wireless sensor networks using support vector machines

S Kaplantzis, A Shilton, N Mani, Y Şekerciǧlu

(2007), pp. 335-340, ISSNIP 2007 : Proceedings of the 2007 International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Vic., E1-1

conference
2005

Incremental training of support vector machines

A Shilton, M Palaniswami, D Ralph, A Tsoi

(2005), Vol. 16, pp. 114-131, IEEE transactions on neural networks, Piscataway, N.J., C1-1

journal article

A convergence rate estimate for the SVM decomposition method

D Lai, A Shilton, N Mani, M Palaniswami

(2005), Vol. 2, pp. 931-936, Proceedings of the International Joint Conference on Neural Networks, Montreal, Que., E1-1

conference
2002

Adaptive support vector machines for regression

M Palaniswami, A Shilton

(2002), Vol. 2, pp. 1043-1049, ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age, Singapore, E1-1

conference

Distributed data fusion using support vector machines

S Challa, M Palaniswami, A Shilton

(2002), Vol. 2, pp. 881-885, Proceedings of the 5th International Conference on Information Fusion, FUSION 2002, Annapolis, MD, E1-1

conference

Funded Projects at Deakin

No Funded Projects at Deakin found

Supervisions

Associate Supervisor
2020

Majid Abdolshah

Thesis entitled: Multi-objective Bayesian Optimisation and Its Applications

Doctor of Philosophy (Information Technology), Applied Artificial Intel Ins