| 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 | 39* | 2023 |
| 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 | 34 | 2022 |
| Monotonic Gaussian Process Flow I Ustyuzhaninov, I Kazlauskaite, CH Ek, NDF Campbell International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020 | 28 | 2020 |
| 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 | 27 | 2020 |
| Gaussian Process Latent Variable Alignment Learning I Kazlauskaite, CH Ek, NDF Campbell International Conference on Artificial Intelligence and Statistics (AISTATS …, 2019 | 27 | 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 | 25 | 2019 |
| AI for Science: an emerging agenda P Berens, K Cranmer, ND Lawrence, U von Luxburg, J Montgomery arXiv preprint arXiv:2303.04217, 2023 | 20 | 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 | 18 | 2023 |
| Calculations of extreme sea level rise scenarios are strongly dependent on ice sheet model resolution CR Williams, P Thodoroff, RJ Arthern, J Byrne, JS Hosking, M Kaiser, ... Communications Earth & Environment 6 (1), 60, 2025 | 11* | 2025 |
| Variational Bayesian surrogate modelling with application to robust design optimisation TA Archbold, I Kazlauskaite, F Cirak Computer Methods in Applied Mechanics and Engineering 432, 117423, 2024 | 7 | 2024 |
| 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 | 7 | 2022 |
| The Bayesian approach to inverse robin problems AK Rasmussen, F Seizilles, M Girolami, I Kazlauskaite SIAM/ASA Journal on Uncertainty Quantification 12 (3), 1050-1084, 2024 | 6 | 2024 |
| Statistical finite elements via interacting particle langevin dynamics A Glyn-Davies, C Duffin, I Kazlauskaite, M Girolami, ÖD Akyildiz SIAM/ASA Journal on Uncertainty Quantification 13 (3), 1200-1227, 2025 | 4 | 2025 |
| 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 | 4 | 2022 |
| Multi‐View Bayesian Optimisation in an Input‐Output Reduced Space for Engineering Design TA Archbold, I Kazlauskaite, F Cirak International Journal for Numerical Methods in Engineering 126 (23), e70187, 2025 | 1 | 2025 |
| A primer on variational inference for physics-informed deep generative modelling A Glyn-Davies, A Vadeboncoeur, OD Akyildiz, I Kazlauskaite, M Girolami Philosophical Transactions A 383 (2299), 20240324, 2025 | 1 | 2025 |
| 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 |
| Spatial and temporal dependence in distribution‐based evaluation of CMIP6 daily maximum temperatures M Virdee, I Kazlauskaite, EJD Boland, E Shuckburgh, A Ming Atmospheric Science Letters 26 (2), e1290, 2025 | | 2025 |
| Limits to the predictability of extreme heat events M Virdee, E Shuckburgh, I Kazlauskaite, E Boland, A Ming AGU Fall Meeting Abstracts 2023, GC33A-03, 2023 | | 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 |
Mark GirolamiUniversity of CambridgeVerified email at cam.ac.uk
