research
∙
02/16/2023
Temporal Graph Neural Networks for Irregular Data
This paper proposes a temporal graph neural network model for forecastin...
share
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...
research
∙
10/21/2022
Calibration tests beyond classification
Most supervised machine learning tasks are subject to irreducible predic...
research
∙
10/14/2022
A Variational Perspective on Generative Flow Networks
Generative flow networks (GFNs) are a class of models for sequential sam...
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 ...
research
∙
06/10/2022
Scalable Deep Gaussian Markov Random Fields for General Graphs
Machine learning methods on graphs have proven useful in many applicatio...
research
∙
04/18/2022
Active Learning with Weak Labels for Gaussian Processes
Annotating data for supervised learning can be costly. When the annotati...
research
∙
10/07/2021
Robustness and reliability when training with noisy labels
Labelling of data for supervised learning can be costly and time-consumi...
research
∙
03/23/2020
Markovian Score Climbing: Variational Inference with KL(p||q)
Modern variational inference (VI) uses stochastic gradients to avoid int...
research
∙
02/26/2020
A general framework for ensemble distribution distillation
Ensembles of neural networks have been shown to give better performance ...
research
∙
02/18/2020
Deep Gaussian Markov random fields
Gaussian Markov random fields (GMRFs) are probabilistic graphical models...
research
∙
10/30/2019
Parameter elimination in particle Gibbs sampling
Bayesian inference in state-space models is challenging due to high-dime...
research
∙
10/24/2019
Calibration tests in multi-class classification: A unifying framework
In safety-critical applications a probabilistic model is usually require...
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...
research
∙
03/12/2019
Elements of Sequential Monte Carlo
A core problem in statistics and probabilistic machine learning is to co...
research
∙
02/19/2019
Evaluating model calibration in classification
Probabilistic classifiers output a probability distribution on target cl...
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...
research
∙
01/08/2019
Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Approximate inference in probabilistic graphical models (PGMs) can be gr...
research
∙
06/25/2018
Learning dynamical systems with particle stochastic approximation EM
We present the particle stochastic approximation EM (PSAEM) algorithm fo...
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...
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...
research
∙
08/17/2017
Pseudo-extended Markov chain Monte Carlo
Sampling from the posterior distribution using Markov chain Monte Carlo ...
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...
research
∙
12/29/2016
High-dimensional Filtering using Nested Sequential Monte Carlo
Sequential Monte Carlo (SMC) methods comprise one of the most successful...
research
∙
07/08/2016
Pseudo-Marginal Hamiltonian Monte Carlo
Bayesian inference in the presence of an intractable likelihood function...
research
∙
02/16/2016
Interacting Particle Markov Chain Monte Carlo
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a P...
research
∙
11/17/2015
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
Pseudo-marginal Metropolis-Hastings (pmMH) is a powerful method for Baye...
research
∙
03/20/2015
Sequential Monte Carlo Methods for System Identification
One of the key challenges in identifying nonlinear and possibly non-Gaus...
research
∙
02/12/2015
Quasi-Newton particle Metropolis-Hastings
Particle Metropolis-Hastings enables Bayesian parameter inference in gen...
research
∙
02/09/2015
Nested Sequential Monte Carlo Methods
We propose nested sequential Monte Carlo (NSMC), a methodology to sample...
research
∙
01/09/2015
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering
Recently, the Frank-Wolfe optimization algorithm was suggested as a proc...
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...
research
∙
02/03/2014
Sequential Monte Carlo for Graphical Models
We propose a new framework for how to use sequential Monte Carlo (SMC) a...
research
∙
01/03/2014
Particle Gibbs with Ancestor Sampling
Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combini...
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...
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...
research
∙
10/25/2012