Ieva Kazlauskaite — accepted AI-conference papers

2023

Random Grid Neural Processes for Parametric Partial Differential Equations

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

ICML 2023poster

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 variational neural processes. We achieve this by assigning probability measures to the spatial domain, which allows us to…

Cited by 15 Source ↗PDF ↗Scholar ↗

2022

Aligned Multi-Task Gaussian Process

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

AISTATS 2022poster

Multi-task learning requires accurate identification of the correlations between tasks. In real-world time-series, tasks are rarely perfectly temporally aligned; traditional multi-task models do not account for this and subsequent errors in correlation estimation will result in poor predictive perfo…

Cited by 2 Source ↗PDF ↗code ↗Scholar ↗

2020

Compositional uncertainty in deep Gaussian processes

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

UAI 2020poster

Gaussian processes (GPs) are nonparametric priors over functions. Fitting a GP implies computing a posterior distribution of functions consistent with the observed data. Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple function…

Cited by 24 Source ↗PDF ↗Scholar ↗

2020

Modulating Surrogates for Bayesian Optimization

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

ICML 2020poster

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 noise-free observations can be collected. Common approaches, which try to model the objective as precisely as possible,…

Cited by 17 Source ↗PDF ↗code ↗Scholar ↗

2020

Monotonic Gaussian Process Flows

Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill Campbell

AISTATS 2020poster

We propose a new framework for imposing monotonicity constraints in a Bayesian non-parametric setting based on numerical solutions of stochastic differential equations. We derive a nonparametric model of monotonic functions that allows for interpretable priors and principled quantification of hierar…

Cited by 25 Source ↗PDF ↗Scholar ↗

2019

Gaussian Process Latent Variable Alignment Learning

Ieva Kazlauskaite, Carl Henrik Ek, Neill Campbell

AISTATS 2019poster

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 where both alignment and data are modelled simultaneously. Further, we automatically infer groupings of different types of s…

Cited by 26 Source ↗PDF ↗Scholar ↗