Search Results for author: Ieva Kazlauskaite

Found 7 papers, 3 papers with code

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 +2

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.

Time Series

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.

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

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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