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Ieva Kazlauskaite
Ieva Kazlauskaite
London School of Economics and Political Science
Adresă de e-mail confirmată pe lse.ac.uk
Titlu
Citat de
Citat de
Anul
Gaussian Process Latent Variable Alignment Learning
I Kazlauskaite, CH Ek, NDF Campbell
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2019
262019
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
242023
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
242022
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
242020
Monotonic Gaussian Process Flow
I Ustyuzhaninov, I Kazlauskaite, CH Ek, NDF Campbell
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020
242020
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
182019
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
142023
AI for Science: an emerging agenda
P Berens, K Cranmer, ND Lawrence, U von Luxburg, J Montgomery
arXiv preprint arXiv:2303.04217, 2023
92023
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
52022
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
32024
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
22022
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
1*2025
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
12024
Sequence Alignment with Dirichlet Process Mixtures
I Kazlauskaite, I Ustyuzhaninov, CH Ek, NDF Campbell
NeurIPS, Workshop on Bayesian Nonparametrics (BNP@NeurIPS), 2018, 2018
12018
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
Multi-view Bayesian optimisation in reduced dimension for engineering design
TA Archbold, I Kazlauskaite, F Cirak
arXiv preprint arXiv:2501.01552, 2025
2025
Statistical Finite Elements via Interacting Particle Langevin Dynamics
A Glyn-Davies, C Duffin, I Kazlauskaite, M Girolami, ÖD Akyildiz
arXiv preprint arXiv:2409.07101, 2024
2024
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling
A Glyn-Davies, A Vadeboncoeur, OD Akyildiz, I Kazlauskaite, M Girolami
arXiv preprint arXiv:2409.06560, 2024
2024
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
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