1 code implementation • Proceedings of The 26th International Conference on Artificial Intelligence and Statistics 2023 • Aidan Scannell, Carl Henrik Ek, Arthur Richards
We present a nonparametric dynamic model which learns the mode constraint alongside the dynamic modes.
1 code implementation • 20 Dec 2022 • Alison Pouplin, David Eklund, Carl Henrik Ek, Søren Hauberg
Generative models are often stochastic, causing the data space, the Riemannian metric, and the geodesics, to be stochastic as well.
no code implementations • 26 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.
no code implementations • pproximateinference AABI Symposium 2022 • David Lopes Fernandes, Francisco Vargas, Carl Henrik Ek, Neill D. F. Campbell
We present a variational inference scheme to learn a model that solves the Schrödinger Bridge Problem (SBP).
no code implementations • 29 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.
no code implementations • NeurIPS 2021 • Vincent Dutordoir, James Hensman, Mark van der Wilk, Carl Henrik Ek, Zoubin Ghahramani, Nicolas Durrande
This results in models that can either be seen as neural networks with improved uncertainty prediction or deep Gaussian processes with increased prediction accuracy.
1 code implementation • 26 Oct 2020 • Erik Bodin, Zhenwen Dai, Neill D. F. Campbell, Carl Henrik Ek
We present a novel approach to Bayesian inference and general Bayesian computation that is defined through a sequential decision loop.
no code implementations • 27 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.
1 code implementation • 17 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.
no code implementations • 10 Jul 2019 • Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek
In this paper, we present a Bayesian view on model-based reinforcement learning.
Model-based Reinforcement Learning
reinforcement-learning
+2
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.
1 code implementation • 30 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.
1 code implementation • 13 Dec 2018 • Alessandro Di Martino, Erik Bodin, Carl Henrik Ek, Neill D. F. Campbell
The shape of an object is an important characteristic for many vision problems such as segmentation, detection and tracking.
no code implementations • 26 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).
no code implementations • 16 Oct 2018 • Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek
The data association problem is concerned with separating data coming from different generating processes, for example when data come from different data sources, contain significant noise, or exhibit multimodality.
no code implementations • 12 Jul 2018 • Andrew R. Lawrence, Carl Henrik Ek, Neill D. F. Campbell
We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting.
1 code implementation • 7 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.
no code implementations • 18 Dec 2017 • Erik Bodin, Iman Malik, Carl Henrik Ek, Neill D. F. Campbell
We would like to learn latent representations that are low-dimensional and highly interpretable.
no code implementations • NeurIPS 2018 • Markus Kaiser, Clemens Otte, Thomas Runkler, Carl Henrik Ek
We apply the method to the real-world problem of finding common structure in the sensor data of wind turbines introduced by the underlying latent and turbulent wind field.
1 code implementation • 11 Aug 2017 • Iman Malik, Carl Henrik Ek
Music is an expressive form of communication often used to convey emotion in scenarios where "words are not enough".
no code implementations • 18 Jul 2017 • Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek
We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs.
no code implementations • 12 Jan 2017 • Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek
We present Manifold Alignment Determination (MAD), an algorithm for learning alignments between data points from multiple views or modalities.
no code implementations • 27 Jul 2016 • Cheng Zhang, Hedvig Kjellstrom, Carl Henrik Ek, Bo C. Bertilson
The positive result indicates a significant potential of machine learning to be used for parts of the pain diagnostic process and to be a decision support system for physicians and other health care personnel.
no code implementations • 30 Jun 2016 • Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek, Neil Lawrence
Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data.
no code implementations • 19 May 2016 • Cheng Zhang, Hedvig Kjellstrom, Carl Henrik Ek
The structured representation leads to a model that marries benefits traditionally associated with a discriminative approach, such as feature selection, with those of a generative model, such as principled regularization and ability to handle missing data.
no code implementations • 17 Apr 2016 • Andreas Damianou, Neil D. Lawrence, Carl Henrik Ek
Inter-battery factor analysis extends this notion to multiple views of the data.
no code implementations • 26 Jan 2015 • Andrea Baisero, Florian T. Pokorny, Carl Henrik Ek
In many applications data is naturally presented in terms of orderings of some basic elements or symbols.
no code implementations • 24 Nov 2014 • Ali Sharif Razavian, Hossein Azizpour, Atsuto Maki, Josephine Sullivan, Carl Henrik Ek, Stefan Carlsson
Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary information associated with the capture of the image or the variation within the object class.
no code implementations • 15 Jan 2013 • Cheng Zhang, Carl Henrik Ek, Andreas Damianou, Hedvig Kjellstrom
In this paper we present a modification to a latent topic model, which makes the model exploit supervision to produce a factorized representation of the observed data.