31 citations · 75 across the 18 of their papers we have counts for
20 papers
Sequential Structure-Sensitive Residual Diagnostics for PDE Inverse Problems
Ieva Kazlauskaite
Computational models in science and engineering are often assessed by checking whether the residual norm is consistent with the assumed noise level. This can be misleading in smoot…
Conformal Risk Sharing: Certified Cost Allocation with Participation Guarantees
Ieva Kazlauskaite
Sharing the financial impact of rare adverse events across a group can soften extreme individual burdens, but any participant made worse off by the arrangement has reason to leave.…
Multi-view Bayesian optimisation in an input-output reduced space for engineering design
Thomas A. Archbold, Ieva Kazlauskaite, Fehmi Cirak
Bayesian optimisation is an adaptive sampling strategy for constructing a Gaussian process surrogate to efficiently search for the global minimum of a black-box computational model…
Statistical Finite Elements via Interacting Particle Langevin Dynamics
Alex Glyn-Davies, Connor Duffin, Ieva Kazlauskaite +2
In this paper, we develop a class of interacting particle Langevin algorithms to solve inverse problems for partial differential equations (PDEs). In particular, we leverage the st…
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling
Alex Glyn-Davies, Arnaud Vadeboncoeur, O. Deniz Akyildiz +2
Variational inference (VI) is a computationally efficient and scalable methodology for approximate Bayesian inference. It strikes a balance between accuracy of uncertainty quantifi…
Variational Bayesian surrogate modelling with application to robust design optimisation
Thomas A. Archbold, Ieva Kazlauskaite, Fehmi Cirak
Surrogate models provide a quick-to-evaluate approximation to complex computational models and are essential for multi-query problems like design optimisation. The inputs of curren…
The Bayesian approach to inverse Robin problems
Aksel Kaastrup Rasmussen, Fanny Seizilles, Mark Girolami +1
In this paper we investigate the Bayesian approach to inverse Robin problems. These are problems for certain elliptic boundary value problems of determining a Robin coefficient on…
Multi-fidelity experimental design for ice-sheet simulation
Pierre Thodoroff, Markus Kaiser, Rosie Williams +5
Computer simulations are becoming an essential tool in many scientific fields from molecular dynamics to aeronautics. In glaciology, future predictions of sea level change require…
Random Grid Neural Processes for Parametric Partial Differential Equations
Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou +3
We introduce a new class of spatially stochastic physics and data informed deep latent models for parametric partial differential equations (PDEs) which operate through scalable va…
A locally time-invariant metric for climate model ensemble predictions of extreme risk
Mala Virdee, Markus Kaiser, Emily Shuckburgh +2
Adaptation-relevant predictions of climate change are often derived by combining climate model simulations in a multi-model ensemble. Model evaluation methods used in performance-b…
Ice Core Dating using Probabilistic Programming
Aditya Ravuri, Tom R. Andersson, Ieva Kazlauskaite +5
Ice cores record crucial information about past climate. However, before ice core data can have scientific value, the chronology must be inferred by estimating the age as a functio…
Fully probabilistic deep models for forward and inverse problems in parametric PDEs
Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite +2
We introduce a physics-driven deep latent variable model (PDDLVM) to learn simultaneously parameter-to-solution (forward) and solution-to-parameter (inverse) maps of parametric par…
Aligned Multi-Task Gaussian Process
Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne +3
Multi-task learning requires accurate identification of the correlations between tasks. In real-world time-series, tasks are rarely perfectly temporally aligned; traditional multi-…
Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices
Jan Povala, Ieva Kazlauskaite, Eky Febrianto +2
Inverse problems involving partial differential equations (PDEs) are widely used in science and engineering. Although such problems are generally ill-posed, different regularisatio…
Bayesian nonparametric shared multi-sequence time series segmentation
Olga Mikheeva, Ieva Kazlauskaite, Hedvig Kjellström +1
In this paper, we introduce a method for segmenting time series data using tools from Bayesian nonparametrics. We consider the task of temporal segmentation of a set of time series…
Compositional uncertainty in deep Gaussian processes
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser +3
Gaussian processes (GPs) are nonparametric priors over functions. Fitting a GP implies computing a posterior distribution of functions consistent with the observed data. Similarly,…
Modulating Surrogates for Bayesian Optimization
Erik Bodin, Markus Kaiser, Ieva Kazlauskaite +3
Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if…
Monotonic Gaussian Process Flow
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek +1
We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations. We derive a…
Sequence Alignment with Dirichlet Process Mixtures
Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek +1
We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM). We follow the approach in…
Gaussian Process Latent Variable Alignment Learning
Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell
We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner. Our proposed method casts alignment learning in a framework wher…