Fredrik Lindsten

research

∙ 02/16/2023

Temporal Graph Neural Networks for Irregular Data

This paper proposes a temporal graph neural network model for forecastin...

0 Joel Oskarsson, et al. ∙

research

∙ 10/26/2022

Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method

Identification of nonlinear systems is a challenging problem. Physical k...

0 Anna Wigren, et al. ∙

research

∙ 10/21/2022

Calibration tests beyond classification

Most supervised machine learning tasks are subject to irreducible predic...

0 David Widmann, et al. ∙

research

∙ 10/14/2022

A Variational Perspective on Generative Flow Networks

Generative flow networks (GFNs) are a class of models for sequential sam...

0 Heiko Zimmermann, et al. ∙

research

∙ 10/13/2022

Marginalized particle Gibbs for multiple state-space models coupled through shared parameters

We consider Bayesian inference from multiple time series described by a ...

0 Anna Wigren, et al. ∙

research

∙ 06/10/2022

Scalable Deep Gaussian Markov Random Fields for General Graphs

Machine learning methods on graphs have proven useful in many applicatio...

0 Joel Oskarsson, et al. ∙

research

∙ 04/18/2022

Active Learning with Weak Labels for Gaussian Processes

Annotating data for supervised learning can be costly. When the annotati...

0 Amanda Olmin, et al. ∙

research

∙ 10/07/2021

Robustness and reliability when training with noisy labels

Labelling of data for supervised learning can be costly and time-consumi...

0 Amanda Olmin, et al. ∙

research

∙ 03/23/2020

Markovian Score Climbing: Variational Inference with KL(p||q)

Modern variational inference (VI) uses stochastic gradients to avoid int...

5 Christian A. Naesseth, et al. ∙

research

∙ 02/26/2020

A general framework for ensemble distribution distillation

Ensembles of neural networks have been shown to give better performance ...

11 Jakob Lindqvist, et al. ∙

research

∙ 02/18/2020

Deep Gaussian Markov random fields

Gaussian Markov random fields (GMRFs) are probabilistic graphical models...

21 Per Sidén, et al. ∙

research

∙ 10/30/2019

Parameter elimination in particle Gibbs sampling

Bayesian inference in state-space models is challenging due to high-dime...

6 Anna Wigren, et al. ∙

research

∙ 10/24/2019

Calibration tests in multi-class classification: A unifying framework

In safety-critical applications a probabilistic model is usually require...

14 David Widmann, et al. ∙

research

∙ 10/21/2019

Particle filter with rejection control and unbiased estimator of the marginal likelihood

We consider the combined use of resampling and partial rejection control...

12 Jan Kudlicka, et al. ∙

research

∙ 03/12/2019

Elements of Sequential Monte Carlo

A core problem in statistics and probabilistic machine learning is to co...

0 Christian A. Naesseth, et al. ∙

research

∙ 02/19/2019

Evaluating model calibration in classification

Probabilistic classifiers output a probability distribution on target cl...

6 Juozas Vaicenavicius, et al. ∙

research

∙ 02/04/2019

Constructing the Matrix Multilayer Perceptron and its Application to the VAE

Like most learning algorithms, the multilayer perceptrons (MLP) is desig...

18 Jalil Taghia, et al. ∙

research

∙ 01/08/2019

Graphical model inference: Sequential Monte Carlo meets deterministic approximations

Approximate inference in probabilistic graphical models (PGMs) can be gr...

2 Fredrik Lindsten, et al. ∙

research

∙ 06/25/2018

Learning dynamical systems with particle stochastic approximation EM

We present the particle stochastic approximation EM (PSAEM) algorithm fo...

0 Andreas Svensson, et al. ∙

research

∙ 01/22/2018

Improving the particle filter for high-dimensional problems using artificial process noise

The particle filter is one of the most successful methods for state infe...

0 Anna Wigren, et al. ∙

research

∙ 11/29/2017

Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations

When classical particle filtering algorithms are used for maximum likeli...

0 Andreas Svensson, et al. ∙

research

∙ 08/17/2017

Pseudo-extended Markov chain Monte Carlo

Sampling from the posterior distribution using Markov chain Monte Carlo ...

0 Christopher Nemeth, et al. ∙

research

∙ 02/06/2017

Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution

Probabilistic (or Bayesian) modeling and learning offers interesting pos...

0 Andreas Svensson, et al. ∙

research

∙ 12/29/2016

High-dimensional Filtering using Nested Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods comprise one of the most successful...

0 Christian A. Naesseth, et al. ∙

research

∙ 07/08/2016

Pseudo-Marginal Hamiltonian Monte Carlo

Bayesian inference in the presence of an intractable likelihood function...

0 Fredrik Lindsten, et al. ∙

research

∙ 02/16/2016

Interacting Particle Markov Chain Monte Carlo

We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a P...

0 Tom Rainforth, et al. ∙

research

∙ 11/17/2015

Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables

Pseudo-marginal Metropolis-Hastings (pmMH) is a powerful method for Baye...

0 Johan Dahlin, et al. ∙

research

∙ 03/20/2015

Sequential Monte Carlo Methods for System Identification

One of the key challenges in identifying nonlinear and possibly non-Gaus...

0 Thomas B. Schön, et al. ∙

research

∙ 02/12/2015

Quasi-Newton particle Metropolis-Hastings

Particle Metropolis-Hastings enables Bayesian parameter inference in gen...

0 Johan Dahlin, et al. ∙

research

∙ 02/09/2015

Nested Sequential Monte Carlo Methods

We propose nested sequential Monte Carlo (NSMC), a methodology to sample...

0 Christian A. Naesseth, et al. ∙

research

∙ 01/09/2015

Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering

Recently, the Frank-Wolfe optimization algorithm was suggested as a proc...

0 Simon Lacoste-Julien, et al. ∙

research

∙ 09/25/2014

Identification of jump Markov linear models using particle filters

Jump Markov linear models consists of a finite number of linear state sp...

0 Andreas Svensson, et al. ∙

research

∙ 02/03/2014

Sequential Monte Carlo for Graphical Models

We propose a new framework for how to use sequential Monte Carlo (SMC) a...

0 Christian A. Naesseth, et al. ∙

research

∙ 01/03/2014

Particle Gibbs with Ancestor Sampling

Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combini...

0 Fredrik Lindsten, et al. ∙

research

∙ 12/17/2013

Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM

Gaussian process state-space models (GP-SSMs) are a very flexible family...

0 Roger Frigola, et al. ∙

research

∙ 06/12/2013

Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC

State-space models are successfully used in many areas of science, engin...

0 Roger Frigola, et al. ∙

research

∙ 10/25/2012

Ancestor Sampling for Particle Gibbs

We present a novel method in the family of particle MCMC methods that we...

0 Fredrik Lindsten, et al. ∙

Fredrik Lindsten

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