Dr Alistair Shilton

Kernel Functional Optimisation

A Arun Kumar, A Shilton, S Rana, S Gupta, S Venkatesh

(2021), Vol. 6, pp. 4725-4737, NeurIPS 2021 : Proceedings of the 35th Conference on Neural Information Processing Systems, Virtual Conference, E1

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

Bayesian optimization for categorical and category-specific continuous inputs

D Nguyen, S Gupta, S Rana, A Shilton, S Venkatesh

(2020), pp. 5256-5263, AAAI-20 : Proceedings of the Thirty-fourth AAAI Conference on Artificial Intelligence, New York, N.Y., E1

Bayesian Optimisation for Objective Functions with Varying Smoothness

A Arun Kumar, S Rana, C Li, S Gupta, A Shilton, S Venkatesh

(2019), Vol. 11919 LNAI, pp. 460-472, AI 2019: Advances in Artificial Intelligence 32nd Australasian Joint Conference, Adelaide, SA, Australia, December 2–5, 2019, Proceedings, Adelaide, South Australia, E1

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

Exploiting strategy-space diversity for batch Bayesian optimisation

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

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

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

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

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

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

DPISVM: 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 : Proceedingins of the International Conference on Recent Advances in Internet of Things, Singapore, E1-1

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

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

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

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

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

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

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

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

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

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

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

Iterative fuzzy support vector machine classification

Alistair Shilton, Daniel Lai

(2007), pp. 1396-1401, 2007 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-4, London, ENGLAND, E1-1

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

Stability analysis of the decomposition method for solving Support Vector Machines

D Lai, A Shilton, N Mani, M Palaniswami

(2005), pp. 272-277, 2005 INTERNATIONAL CONFERENCE ON INTELLIGENT SENSING AND INFORMATION PROCESSING, PROCEEDINGS, Chennai, INDIA, E1-1

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

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