Ieva Kazlauskaite
Ieva Kazlauskaite
Bestätigte E-Mail-Adresse bei
Zitiert von
Zitiert von
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
Monotonic Gaussian Process Flow
I Ustyuzhaninov, I Kazlauskaite, CH Ek, NDF Campbell
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2020
Gaussian Process Latent Variable Alignment Learning
I Kazlauskaite, CH Ek, NDF Campbell
International Conference on Artificial Intelligence and Statistics (AISTATS …, 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
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
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
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
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
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
Sequence Alignment with Dirichlet Process Mixtures
I Kazlauskaite, I Ustyuzhaninov, CH Ek, NDF Campbell
NeurIPS, Workshop on Bayesian Nonparametrics (BNP@NeurIPS), 2018, 2018
Limits to the predictability of extreme heat events
M Virdee, E Shuckburgh, I Kazlauskaite, E Boland, A Ming
AGU23, 2023
The Bayesian approach to inverse Robin problems
A Kaastrup Rasmussen, F Seizilles, M Girolami, I Kazlauskaite
arXiv e-prints, arXiv: 2311.17542, 2023
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, ...
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
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
Ice Core Dating using Probabilistic Programming
A Ravuri, TR Andersson, I Kazlauskaite, W Tebbutt, RE Turner, ...
arXiv preprint arXiv:2210.16568, 2022
Bayesian nonparametric shared multi-sequence time series segmentation
O Mikheeva, I Kazlauskaite, H Kjellström, CH Ek
arXiv preprint arXiv:2001.09886, 2020
Compositional uncertainty in models of alignment
I Kazlauskaite
University of Bath, 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
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–19