1 citations · 1 across the 3 of their papers we have counts for
5 papers
A Gauss-Newton Method for Markov Decision Processes
Thomas Furmston, Guy Lever
Approximate Newton methods are a standard optimization tool which aim to maintain the benefits of Newton's method, such as a fast rate of convergence, whilst alleviating its drawba…
A Bayesian Residual-Based Test for Cointegration
Thomas Furmston, Stephen Hailes, A. Jennifer Morton
Cointegration is an important concept in the analysis of non-stationary time-series, giving conditions under which a collection of non-stationary processes has an underlying statio…
Convergence Analysis of the Approximate Newton Method for Markov Decision Processes
Thomas Furmston, Guy Lever
Recently two approximate Newton methods were proposed for the optimisation of Markov Decision Processes. While these methods were shown to have desirable properties, such as a guar…
An Approximate Newton Method for Markov Decision Processes
Thomas Furmston, David Barber
Gradient-based algorithms are one of the methods of choice for the optimisation of Markov Decision Processes. In this article we will present a novel approximate Newton algorithm f…
Efficient Inference in Markov Control Problems
Thomas Furmston, David Barber
Markov control algorithms that perform smooth, non-greedy updates of the policy have been shown to be very general and versatile, with policy gradient and Expectation Maximisation…