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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...
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02/22/2021
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization
Machine learning practitioners invest significant manual and computation...
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12/14/2020
Variational State and Parameter Estimation
This paper considers the problem of computing Bayesian estimates of both...
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12/08/2020
Variational Nonlinear System Identification
This paper considers parameter estimation for nonlinear state-space mode...
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11/09/2020
Practical Bayesian System Identification using Hamiltonian Monte Carlo
This paper addresses Bayesian system identification using a Markov Chain...
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10/14/2020
A Heteroscedastic Likelihood Model for Two-frame Optical Flow
Machine vision is an important sensing technology used in mobile robotic...
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04/24/2020
Bayesian Non-parametric Bragg-edge Fitting for Neutron Transmission Strain Imaging
Energy resolved neutron transmission techniques can provide high-resolut...
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02/07/2020
Constructing a variational family for nonlinear state-space models
We consider the problem of maximum likelihood parameter estimation for n...
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02/05/2020
Linearly Constrained Neural Networks
We present an approach to designing neural network based models that wil...
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10/18/2019
Learning Continuous Occupancy Maps with the Ising Process Model
We present a new method of learning a continuous occupancy field for use...
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09/04/2019
Deep kernel learning for integral measurements
Deep kernel learning refers to a Gaussian process that incorporates neur...
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09/03/2019
Stochastic quasi-Newton with line-search regularization
In this paper we present a novel quasi-Newton algorithm for use in stoch...
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09/29/2018
A fast quasi-Newton-type method for large-scale stochastic optimisation
During recent years there has been an increased interest in stochastic a...
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06/26/2018
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
Pseudo-marginal Metropolis-Hastings (pmMH) is a versatile algorithm for ...
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02/12/2018
Stochastic quasi-Newton with adaptive step lengths for large-scale problems
We provide a numerically robust and fast method capable of exploiting th...
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01/04/2018
Constructing Metropolis-Hastings proposals using damped BFGS updates
This paper considers the problem of computing Bayesian estimates of syst...
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01/04/2018
Sparse Bayesian ARX models with flexible noise distributions
This paper considers the problem of estimating linear dynamic system mod...
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03/02/2017
Linearly constrained Gaussian processes
We consider a modification of the covariance function in Gaussian proces...
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02/12/2015