| Multi-resolution multi-task Gaussian processes O Hamelijnck, T Damoulas, K Wang, M Girolami Advances in Neural Information Processing Systems 32, 2019 | 45 | 2019 |
| Non-separable Non-stationary random fields K Wang, O Hamelijnck, T Damoulas, M Steel International Conference on Machine Learning, 9887-9897, 2020 | 13 | 2020 |
| Nonstationary nonseparable random fields K Wang, O Hamelijnck, T Damoulas, M Steel Proceedings of the 37th International Conference on Machine Learning (ICML), 2020 | 3 | 2020 |
| Deep Bayesian Supervised Learning given Hypercuboidally-shaped, Discontinuous Data, using Compound Tensor-Variate & Scalar-Variate Gaussian Processes K Wang, D Chakrabarty arXiv preprint arXiv:1803.04582, 2018 | 1 | 2018 |
| Constructing training set using distance between learnt graphical models of time series data on patient physiology, to predict disease scores D Chakrabarty, K Wang, G Roy, A Bhojgaria, C Zhang, J Pavlu, ... Plos one 18 (10), e0292404, 2023 | | 2023 |
| Soft Random Graphs in Probabilistic Metric Spaces & Inter-graph Distance K Wang, D Chakrabarty arXiv preprint arXiv:2002.01339, 2020 | | 2020 |
| High-Dimensional Bayesian Non-parametric Learning of System Parameters in Different Data Scenarios K Wang University of Leicester, 2018 | | 2018 |
| Correlation between Multivariate Datasets, from Inter-Graph Distance computed using Graphical Models Learnt With Uncertainties K Wang, D Chakrabarty arXiv preprint arXiv:1710.11292, 2017 | | 2017 |
| Bayesian Covariance Modelling of Large Tensor-Variate Data Sets Inverse Non-parametric Learning of the Unknown Model Parameter Vector K Wang, D Chakrabarty arXiv preprint arXiv:1512.05538, 2015 | | 2015 |
| Uncertainty in Test Score Data and Classically Defined Reliability of Tests and Test Batteries, using a New Method for Test Dichotomisation SN Chakrabartty, K Wang, D Chakrabarty arXiv preprint arXiv:1503.03297, 2015 | | 2015 |
| Supplementary Material for Multi-resolution Multi-task Gaussian Processes O Hamelijnck, T Damoulas, K Wang, MA Girolami | | |
