Ieva Kazlauskaite

24 papers A* 2A 4Journal 17

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Multi-view Bayesian optimisation in reduced dimension for engineering design. CoRR Thomas A. Archbold, Ieva Kazlauskaite, Fehmi Cirak
2025	J	`jnl`	Statistical Finite Elements via Interacting Particle Langevin Dynamics. SIAM/ASA J. Uncertain. Quantification Alex Glyn-Davies, Connor Duffin, Ieva Kazlauskaite, Mark Girolami, Ömer Deniz Akyildiz
2024	J	`jnl`	A Primer on Variational Inference for Physics-Informed Deep Generative Modelling. CoRR Alex Glyn-Davies, Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami
2024	J	`jnl`	The Bayesian Approach to Inverse Robin Problems. SIAM/ASA J. Uncertain. Quantification Aksel K. Rasmussen, Fanny Seizilles, Mark Girolami, Ieva Kazlauskaite
2024	J	`jnl`	Variational Bayesian surrogate modelling with application to robust design optimisation. CoRR Thomas A. Archbold, Ieva Kazlauskaite, Fehmi Cirak
2023	J	`jnl`	Fully probabilistic deep models for forward and inverse problems in parametric PDEs. J. Comput. Phys. Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak
2023	A*	`conf`	Random Grid Neural Processes for Parametric Partial Differential Equations. ICML Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz
2023	J	`jnl`	Random Grid Neural Processes for Parametric Partial Differential Equations. CoRR Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz
2022	A	`conf`	Aligned Multi-Task Gaussian Process. AISTATS Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne, Hedvig Kjellström, Carl Henrik Ek, Neill D. F. Campbell
2022	J	`jnl`	Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs. CoRR Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak
2022	J	`jnl`	Ice Core Dating using Probabilistic Programming. CoRR Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite, Will Tebbutt, Richard E. Turner, J. Scott Hosking, Neil D. Lawrence, Markus Kaiser
2022	J	`jnl`	Optimisation of a global climate model ensemble for prediction of extreme heat days. CoRR Mala Virdee, Markus Kaiser, Emily Shuckburgh, Carl Henrik Ek, Ieva Kazlauskaite
2021	J	`jnl`	Aligned Multi-Task Gaussian Process. CoRR Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne, Hedvig Kjellström, Carl Henrik Ek, Neill D. F. Campbell
2020	J	`jnl`	Bayesian nonparametric shared multi-sequence time series segmentation. CoRR Olga Mikheeva, Ieva Kazlauskaite, Hedvig Kjellström, Carl Henrik Ek
2020	A	`conf`	Compositional uncertainty in deep Gaussian processes. UAI Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek
2020	—	`—`	Compositional uncertainty in models of alignment Ieva Kazlauskaite
2020	A*	`conf`	Modulating Surrogates for Bayesian Optimization. ICML Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill W. Campbell, Carl Henrik Ek
2020	A	`conf`	Monotonic Gaussian Process Flows. AISTATS Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell
2019	J	`jnl`	Compositional uncertainty in deep Gaussian processes. CoRR Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek
2019	A	`conf`	Gaussian Process Latent Variable Alignment Learning. AISTATS Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell
2019	J	`jnl`	Modulated Bayesian Optimization using Latent Gaussian Process Models. CoRR Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Neill D. F. Campbell, Carl Henrik Ek
2019	J	`jnl`	Monotonic Gaussian Process Flow. CoRR Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell
2018	J	`jnl`	Gaussian Process Latent Variable Alignment Learning. CoRR Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell
2018	J	`jnl`	Sequence Alignment with Dirichlet Process Mixtures. CoRR Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek, Neill D. F. Campbell