Ieva Kazlauskaite

According to our database, Ieva Kazlauskaite authored at least 18 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

The Bayesian Approach to Inverse Robin Problems.

Aksel K. Rasmussen, Fanny Seizilles, Mark Girolami, Ieva Kazlauskaite

SIAM/ASA J. Uncertain. Quantification, 2024

A Primer on Variational Inference for Physics-Informed Deep Generative Modelling.

Alex Glyn-Davies, Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami

CoRR, 2024

Variational Bayesian surrogate modelling with application to robust design optimisation.

Thomas A. Archbold, Ieva Kazlauskaite, Fehmi Cirak

CoRR, 2024

2023

Fully probabilistic deep models for forward and inverse problems in parametric PDEs.

Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak

J. Comput. Phys., October, 2023

Random Grid Neural Processes for Parametric Partial Differential Equations.

Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz

Proceedings of the International Conference on Machine Learning, 2023

2022

Optimisation of a global climate model ensemble for prediction of extreme heat days.

Mala Virdee, Markus Kaiser, Emily Shuckburgh, Carl Henrik Ek, Ieva Kazlauskaite

CoRR, 2022

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

CoRR, 2022

Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs.

Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak

CoRR, 2022

Aligned Multi-Task Gaussian Process.

Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne, Hedvig Kjellström, Carl Henrik Ek, Neill D. F. Campbell

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2020

Compositional uncertainty in models of alignment

Ieva Kazlauskaite

PhD thesis, 2020

Bayesian nonparametric shared multi-sequence time series segmentation.

Olga Mikheeva, Ieva Kazlauskaite, Hedvig Kjellström, Carl Henrik Ek

CoRR, 2020

Compositional uncertainty in deep Gaussian processes.

Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Modulating Surrogates for Bayesian Optimization.

Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill W. Campbell, Carl Henrik Ek

Proceedings of the 37th International Conference on Machine Learning, 2020

Monotonic Gaussian Process Flows.

Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Modulated Bayesian Optimization using Latent Gaussian Process Models.

Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Neill D. F. Campbell, Carl Henrik Ek

CoRR, 2019

Monotonic Gaussian Process Flow.

Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell

CoRR, 2019

Gaussian Process Latent Variable Alignment Learning.

Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Sequence Alignment with Dirichlet Process Mixtures.

Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek, Neill D. F. Campbell

CoRR, 2018