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Results for: Alistair Shilton

Bayesian Optimization for Categorical and Category-Specific Continuous Inputs

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

2020 / Vol. 34, pp. 5256 - 5263 / Proceedings of the AAAI Conference on Artificial Intelligence

Accelerated Bayesian Optimisation through Weight-Prior Tuning

A. Shilton, S. Gupta, S. Rana, P. Vellanki, C. Li, S. Venkatesh, L. Park, A. Sutti, D. Rubin, T. Dorin, O.

2020 / pp. 635 - 645 / Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)

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

Multi-Objective Bayesian Optimisation with Preferences over Objectives

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

2019 / pp. 12214 - 12224 / Advances in Neural Information Processing Systems

Bayesian Optimisation for Objective Functions with Varying Smoothness

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

2019 / pp. 460 - 472 / Australasian Joint Conference on Artificial Intelligence

Multi-Target Optimisation via Bayesian Optimisation and Linear Programming

A. Shilton, S. Rana, S. Gupta, S. Venkatesh

2018 / UAI 2018: Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence

Expected Hypervolume Improvement with Constraints

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

2018 / pp. 3238 - 3243 / International Conference on Pattern Recognition (ICPR)

High Dimensional Bayesian Optimization Using Dropout

C. Li, S. Gupta, S. Rana, V. Nguyen, S. Venkatesh, A. Shilton

2017 / pp. 2096 - 2102 / International Joint Conference on Artificial Intelligence

A Simple Recursive Algorithm for Calculating Expected Hypervolume Improvement

A. Shilton, S. Rana, S. Gupta, S. Venkatesh

2016 / pp. 1 - 5 / Proceedings of NIPS Workshop on Bayesian Optimization: Black-Box Optimization and beyond, BayesOpt 2016

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, J. Yearwood, N. Dimitrova, T. Ho, O.

2016 / Vol. 18, pp. e323 / Journal of medical Internet research