A convergence rate estimate for the SVM decomposition method. - Daniel T. H. Lai; Alistair Shilton; Nallasamy Mani; Marimuthu Palaniswami (IJCNN, 2005)

Incremental training of support vector machines. - Alistair Shilton; Marimuthu Palaniswami; Daniel Ralph; Ah Chung Tsoi (IEEE Trans. Neural Networks, 2005)

Real Value Solvent Accessibility Prediction using Adaptive Support Vector Regression. - Jayavardhana Gubbi; Alistair Shilton; Marimuthu Palaniswami; Michael Parker (CIBCB, 2007)

Iterative Fuzzy Support Vector Machine Classification. - Alistair Shilton; Daniel T. H. Lai (FUZZ-IEEE, 2007)

Quaternionic and complex-valued Support Vector Regression for Equalization and Function Approximation. - Alistair Shilton; Daniel T. H. Lai (IJCNN, 2007)

A Division Algebraic Framework for Multidimensional Support Vector Regression. - Alistair Shilton; Daniel T. H. Lai; Marimuthu Palaniswami (IEEE Trans. Syst. Man Cybern. Part B, 2010)

Automatic detection of different walking conditions using inertial sensor data. - Braveena K. Santhiranayagam; Daniel T. H. Lai; Cancan Jiang; Alistair Shilton; Rezaul K. Begg (IJCNN, 2012)

The conic-segmentation support vector machine - a target space method for multiclass classification. - Alistair Shilton; Daniel T. H. Lai; Marimuthu Palaniswami (IJCNN, 2012)

Fast supersymmetry phenomenology at the Large Hadron Collider using machine learning techniques. - Andy Buckley; Alistair Shilton; M. J. White (Comput. Phys. Commun., 2012)

A Note on Octonionic Support Vector Regression. - Alistair Shilton; Daniel T. H. Lai; Braveena K. Santhiranayagam; Marimuthu Palaniswami (IEEE Trans. Syst. Man Cybern. Part B, 2012)

Autonomous detection of different walking tasks using end point foot trajectory vertical displacement data. - Braveena K. Santhiranayagam; Daniel T. H. Lai; Alistair Shilton; Rezaul K. Begg; Marimuthu Palaniswami (ISSNIP, 2013)

Combined multiclass classification and anomaly detection for large-scale Wireless Sensor Networks. - Alistair Shilton; Sutharshan Rajasegarar; Marimuthu Palaniswami (ISSNIP, 2013)

DP1SVM: A dynamic planar one-class support vector machine for Internet of Things environment. - Alistair Shilton; Sutharshan Rajasegarar; Christopher Leckie; Marimuthu Palaniswami (RIoT, 2015)

Regret Bounds for Transfer Learning in Bayesian Optimisation. - Alistair Shilton; Sunil Gupta 001; Santu Rana; Svetha Venkatesh (AISTATS, 2017)

High Dimensional Bayesian Optimization using Dropout. - Cheng Li 003; Sunil Gupta 001; Santu Rana; Vu Nguyen 001; Svetha Venkatesh; Alistair Shilton (IJCAI, 2017)

Exploiting Strategy-Space Diversity for Batch Bayesian Optimization. - Sunil Gupta 001; Alistair Shilton; Santu Rana; Svetha Venkatesh (AISTATS, 2018)

Expected Hypervolume Improvement with Constraints. - Majid Abdolshah; Alistair Shilton; Santu Rana; Sunil Gupta 001; Svetha Venkatesh (ICPR, 2018)

Multi-Target Optimisation via Bayesian Optimisation and Linear Programming. - Alistair Shilton; Santu Rana; Sunil Gupta 001; Svetha Venkatesh (UAI, 2018)

Kernel Pre-Training in Feature Space via m-Kernels. - Alistair Shilton; Sunil Gupta 001; Santu Rana; Pratibha Vellanki; Cheng Li 003; Svetha Venkatesh; Laurence Park 001; Alessandra Sutti; David Rubin; Thomas Dorin; Alireza Vahid; Murray Height; et al. (CoRR, 2018)

Bayesian Optimisation for Objective Functions with Varying Smoothness. - A. V. Arun Kumar; Santu Rana; Cheng Li 001; Sunil Gupta 001; Alistair Shilton; Svetha Venkatesh (Australasian Conference on Artificial Intelligence, 2019)

Multi-objective Bayesian optimisation with preferences over objectives. - Majid Abdolshah; Alistair Shilton; Santu Rana; Sunil Gupta 001; Svetha Venkatesh (NeurIPS, 2019)

Stable Bayesian Optimisation via Direct Stability Quantification. - Alistair Shilton; Sunil Gupta 001; Santu Rana; Svetha Venkatesh; Majid Abdolshah; Dang Nguyen 002 (CoRR, 2019)

Cost-aware Multi-objective Bayesian optimisation. - Majid Abdolshah; Alistair Shilton; Santu Rana; Sunil Gupta 001; Svetha Venkatesh (CoRR, 2019)

Bayesian Optimization for Categorical and Category-Specific Continuous Inputs. - Dang Nguyen 002; Sunil Gupta 001; Santu Rana; Alistair Shilton; Svetha Venkatesh (AAAI, 2020)

Accelerated Bayesian Optimisation through Weight-Prior Tuning. - Alistair Shilton; Sunil Gupta 001; Santu Rana; Pratibha Vellanki; Cheng Li 003; Svetha Venkatesh; Laurence Park 001; Alessandra Sutti; David Rubin; Thomas Dorin; Alireza Vahid; Murray Height; et al. (AISTATS, 2020)

From deep to Shallow: Equivalent Forms of Deep Networks in Reproducing Kernel Krein Space and Indefinite Support Vector Machines. - Alistair Shilton (CoRR, 2020)

Sequential Subspace Search for Functional Bayesian Optimization Incorporating Experimenter Intuition. - Alistair Shilton; Sunil Gupta 001; Santu Rana; Svetha Venkatesh (CoRR, 2020)

Multiclass Anomaly Detector: the CS++ Support Vector Machine. - Alistair Shilton; Sutharshan Rajasegarar; Marimuthu Palaniswami (J. Mach. Learn. Res., 2020)

Kernel Functional Optimisation. - Arun Kumar Anjanapura Venkatesh; Alistair Shilton; Santu Rana; Sunil Gupta 001; Svetha Venkatesh (NeurIPS, 2021)

Fairness improvement for black-box classifiers with Gaussian process. - Dang Nguyen 002; Sunil Gupta 001; Santu Rana; Alistair Shilton; Svetha Venkatesh (Inf. Sci., 2021)

TRF: Learning Kernels with Tuned Random Features. - Alistair Shilton; Sunil Gupta 001; Santu Rana; Arun Kumar Anjanapura Venkatesh; Svetha Venkatesh (AAAI, 2022)

Human-AI Collaborative Bayesian Optimisation. - Arun Kumar A. V.; Santu Rana; Alistair Shilton; Svetha Venkatesh (NeurIPS, 2022)

Gradient Descent in Neural Networks as Sequential Learning in Reproducing Kernel Banach Space. - Alistair Shilton; Sunil Gupta 001; Santu Rana; Svetha Venkatesh (ICML, 2023)

Gradient Descent in Neural Networks as Sequential Learning in RKBS. - Alistair Shilton; Sunil Gupta 001; Santu Rana; Svetha Venkatesh (CoRR, 2023)

BO-Muse: A human expert and AI teaming framework for accelerated experimental design. - Sunil Gupta 001; Alistair Shilton; Arun Kumar A. V.; Shannon Ryan; Majid Abdolshah; Hung Le 002; Santu Rana; Julian Berk; Mahad Rashid; Svetha Venkatesh (CoRR, 2023)

Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties. - A. V. Arun Kumar; Alistair Shilton; Sunil Gupta 001; Santu Rana; Stewart Greenhill; Svetha Venkatesh (ECML/PKDD (6), 2024)

PINN-BO: A Black-Box Optimization Algorithm Using Physics-Informed Neural Networks. - Dat Phan-Trong; Hung The Tran; Alistair Shilton; Sunil Gupta 001 (ECML/PKDD (2), 2024)

Novel Kernel Models and Exact Representor Theory for Neural Networks Beyond the Over-Parameterized Regime. - Alistair Shilton; Sunil Gupta 001; Santu Rana; Svetha Venkatesh (CoRR, 2024)

Accelerated experimental design using a human-AI teaming framework. - Arun Kumar Anjanapura Venkatesh; Alistair Shilton; Sunil Gupta 001; Shannon Ryan; Majid Abdolshah; Hung Le 002; Santu Rana; Julian Berk; Mahad Rashid; Svetha Venkatesh (Knowl. Based Syst., 2025)