Search Results for author: Ieva Kazlauskaite

Found 11 papers, 4 papers with code

Random Grid Neural Processes for Parametric Partial Differential Equations

no code implementations26 Jan 2023 Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz

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.

A locally time-invariant metric for climate model ensemble predictions of extreme risk

no code implementations26 Nov 2022 Mala Virdee, Markus Kaiser, Emily Shuckburgh, Carl Henrik Ek, Ieva Kazlauskaite

Adaptation-relevant predictions of climate change are often derived by combining climate model simulations in a multi-model ensemble.

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

no code implementations9 Aug 2022 Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak

In the posited probabilistic model, both the forward and inverse maps are approximated as Gaussian distributions with a mean and covariance parameterized by deep neural networks.

Variational Inference

Aligned Multi-Task Gaussian Process

no code implementations29 Oct 2021 Olga Mikheeva, Ieva Kazlauskaite, Adam Hartshorne, Hedvig Kjellström, Carl Henrik Ek, Neill D. F. Campbell

Building on the previous work by Kazlauskaiteet al. [2019], we include a separate monotonic warp of the input data to model temporal misalignment.

Bayesian Inference Gaussian Processes +4

Bayesian nonparametric shared multi-sequence time series segmentation

no code implementations27 Jan 2020 Olga Mikheeva, Ieva Kazlauskaite, Hedvig Kjellström, Carl Henrik Ek

In this paper, we introduce a method for segmenting time series data using tools from Bayesian nonparametrics.

Segmentation Time Series +1

Compositional uncertainty in deep Gaussian processes

1 code implementation17 Sep 2019 Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser, Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek

Similarly, deep Gaussian processes (DGPs) should allow us to compute a posterior distribution of compositions of multiple functions giving rise to the observations.

Bayesian Inference Gaussian Processes +1

Modulating Surrogates for Bayesian Optimization

no code implementations ICML 2020 Erik Bodin, Markus Kaiser, Ieva Kazlauskaite, Zhenwen Dai, Neill D. F. Campbell, Carl Henrik Ek

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.

Bayesian Optimization Gaussian Processes

Monotonic Gaussian Process Flow

1 code implementation30 May 2019 Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell

We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations.

Gaussian Processes Time Series +1

Sequence Alignment with Dirichlet Process Mixtures

no code implementations26 Nov 2018 Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek, Neill D. F. Campbell

We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM).

Gaussian Processes

Gaussian Process Latent Variable Alignment Learning

1 code implementation7 Mar 2018 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.

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