| Compositional uncertainty in deep Gaussian processes I Ustyuzhaninov, I Kazlauskaite, M Kaiser, E Bodin, N Campbell, CH Ek Conference on Uncertainty in Artificial Intelligence, 480-489, 2020 | 21 | 2020 |
| Monotonic Gaussian Process Flow I Ustyuzhaninov, I Kazlauskaite, CH Ek, NDF Campbell International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020 | 21 | 2020 |
| Gaussian Process Latent Variable Alignment Learning I Kazlauskaite, CH Ek, NDF Campbell International Conference on Artificial Intelligence and Statistics (AISTATS …, 2019 | 18 | 2019 |
| Modulating Surrogates for Bayesian Optimization E Bodin, M Kaiser, I Kazlauskaite, Z Dai, NDF Campbell, CH Ek International Conference on Machine Learning (ICML 2020), arXiv: 1906.11152, 2019 | 13 | 2019 |
| Variational Bayesian approximation of inverse problems using sparse precision matrices J Povala, I Kazlauskaite, E Febrianto, F Cirak, M Girolami Computer Methods in Applied Mechanics and Engineering 393, 114712, 2022 | 12 | 2022 |
| Fully probabilistic deep models for forward and inverse problems in parametric PDEs A Vadeboncoeur, ÖD Akyildiz, I Kazlauskaite, M Girolami, F Cirak Journal of Computational Physics 491, 112369, 2023 | 5* | 2023 |
| Random Grid Neural Processes for Parametric Partial Differential Equations A Vadeboncoeur, I Kazlauskaite, Y Papandreou, F Cirak, M Girolami, ... International Conference on Machine Learning, 34759-34778, 2023 | 3 | 2023 |
| Multi-fidelity experimental design for ice-sheet simulation P Thodoroff, M Kaiser, R Williams, R Arthern, S Hosking, N Lawrence, ... NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and …, 2022 | 3 | 2022 |
| Aligned Multi-Task Gaussian Process O Mikheeva, I Kazlauskaite, A Hartshorne, H Kjellström, CH Ek, ... International Conference on Artificial Intelligence and Statistics, 2970-2988, 2022 | 2 | 2022 |
| Modulated Bayesian Optimization using Latent Gaussian Process Models E Bodin, M Kaiser, I Kazlauskaite, NDF Campbell, CH Ek https://arxiv.org/abs/1906.11152, 2019 | 1 | 2019 |
| Sequence Alignment with Dirichlet Process Mixtures I Kazlauskaite, I Ustyuzhaninov, CH Ek, NDF Campbell NeurIPS, Workshop on Bayesian Nonparametrics (BNP@NeurIPS), 2018, 2018 | 1 | 2018 |
| Calculating exposure to extreme sea level risk will require high resolution ice sheet models C Williams, P Thodoroff, R Arthern, J Byrne, JS Hosking, M Kaiser, ... | | 2023 |
| A locally time-invariant metric for climate model ensemble predictions of extreme risk M Virdee, M Kaiser, CH Ek, E Shuckburgh, I Kazlauskaite Environmental Data Science 2, E26, 2023 | | 2023 |
| Probabilistic Machine Learning for Automated Ice Core Dating A Ravuri, T Andersson, M Kaiser, I Kazlauskaite, M Fryer, JS Hosking, ... AGU Fall Meeting 2022, 2022 | | 2022 |
| Ice Core Dating using Probabilistic Programming A Ravuri, TR Andersson, I Kazlauskaite, W Tebbutt, RE Turner, ... arXiv preprint arXiv:2210.16568, 2022 | | 2022 |
| Bayesian nonparametric shared multi-sequence time series segmentation O Mikheeva, I Kazlauskaite, H Kjellström, CH Ek arXiv preprint arXiv:2001.09886, 2020 | | 2020 |
| Compositional uncertainty in models of alignment I Kazlauskaite University of Bath, 2020 | | 2020 |
| Data Study Group final report: NHS Scotland–predicting risk of hospital admission in Scotland B Mateen, F Kiraly, S Vollmer, L Aslett, R Sonabend, I Manolopoulou, ... Zenodo, 2019 | | 2019 |
| Learning Alignments from Latent Space Structures I Kazlauskaite, CH Ek, N Campbell NIPS Workshop on Learning in High Dimensions with Structure, 2016 | | 2016 |
Carl Henrik EkUniversity of CambridgeVerified email at cam.ac.uk
