Ieva Kazlauskaitė

Ieva Kazlauskaitė

Publications

Variational Bayesian surrogate modelling with application to robust design optimisation
Thomas A Archbold, Ieva Kazlauskaite, Fehmi Cirak
arXiv preprint, 2023
[ArXiv]
The Bayesian approach to inverse Robin problems
Aksel Kaastrup Rasmussen, Fanny Seizilles, Mark Girolami, Ieva Kazlauskaite
To appear in SIAM Journal on Uncertainty Quantification (JUQ), 2023
[ArXiv]
Fully probabilistic deep models for forward and inverse problems in parametric PDEs
Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak
Journal of Computational Physics, 2022
[ArXiv]
A locally time-invariant metric for climate model ensemble predictions of extreme risk
Mala Virdee, Markus Kaiser, Emily Shuckburgh, Carl Henrik Ek, Ieva Kazlauskaite
Environmental Data Science, 2023
[ArXiv]
Random Grid Neural Processes for Parametric Partial Differential Equations
Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz
International Conference on Machine Learning (ICML), 2023
[ArXiv]
Ice Core Dating using Probabilistic Programming
Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser
NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022
[Poster] [ArXiv]
Probabilistic Machine Learning for Automated Ice Core Dating
Aditya Ravuri, Tom Andersson, Markus Kaiser, Ieva Kazlauskaite, Max Fryer, J Scott Hosking, Mark Girolami, Neil D Lawrence.
AGU Fall Meeting, 2022
[Paper] [Poster]
Multi-fidelity experimental design for ice-sheet simulation
Pierre Thodoroff, Markus Kaiser, Rosie Williams, Robert Arthern, J. Scott Hosking, Neil D. Lawrence, Ieva Kazlauskaite
NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems, 2022
[Paper] [Code] [Poster]
Aligned Multi-Task Gaussian Process
Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne, Hedvig Kjellström, Carl Henrik Ek, Neill Campbell
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
[Paper] [ArXiv]
Variational Bayesian approximation of inverse problems using sparse precision matrices
Jan Povala*, Ieva Kazlauskaite* , Eky Febrianto, Fehmi Cirak, Mark Girolami.
Computer Methods in Applied Mechanics and Engineering, 2022
[Paper] [Code] [ArXiv]
Monotonic Gaussian Process Flow
Ivan Ustyuzhaninov*, Ieva Kazlauskaite* , Carl Henrik Ek, Neill DF Campbell
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
[Paper] [Code] [ArXiv]
Compositional uncertainty in deep Gaussian processes
Ivan Ustyuzhaninov*, Ieva Kazlauskaite* , Markus Kaiser, Erik Bodin, Neill Campbell, Carl Henrik Ek
Conference on Uncertainty in Artificial Intelligence (UAI), 2020
[Paper] [Code] [ArXiv]
Modulating Surrogates for Bayesian Optimization
Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill DF Campbell, Carl Henrik Ek
International Conference on Machine Learning (ICML), 2020
[Paper] [ArXiv]
Gaussian Process Latent Variable Alignment Learning
Ieva Kazlauskaite, Carl Henrik Ek, Neill DF Campbell
International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
[Paper] [Code] [Poster] [ArXiv]