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ANC Workshop Talk: Amos Storkey and Simon Lyons, Chair: Ioan Stanculescu

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  • ANC Workshop Talk
When Jun 19, 2012
from 11:00 AM to 12:00 PM
Where IF 4.31/4.33
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Amos Sotrkey

Isoelastic Agents and Wealth Updates in Machine Learning Markets

Recently, prediction markets have shown considerable promise for developing flexible mechanisms for machine learning. In this paper agents with isoelastic utilities are considered, and it is shown that the costs associated with homogeneous markets of agents with isoelastic utilities produce equilibrium prices corresponding to alpha-mixtures, with a particular form of mixing component relating to each agent's wealth. We also demonstrate that wealth accumulation for logarithmic and other isoelastic agents (through payoffs on prediction of training targets) can implement both Bayesian model updates and mixture weight updates by imposing different market payoff structures. An efficient variational algorithm is given for market equilibrium computation. We demonstrate that inhomogeneous markets of agents with isoelastic utilities outperform state of the art aggregate classifiers such as random forests, as well as single classifiers (neural networks, decision trees) on a number of machine learning benchmarks, and show that isoelastic combination methods are generally better than using logarithmic agents.  

Amos Storkey, Jono Millin, Krzysztof Geras

Simon Lyons

Stochastic differential equations and the coloured noise approximation

Stochastic differential equations (SDE) are a natural tool for modelling systems that are inherently noisy or contain uncertainties that can be modelled as stochastic processes. In this talk, I will describe a novel method of approximating a stochastic differential equation.
We take the ‘white’ noise that drives a diffusion process and decompose it into two terms. The first is a ‘coloured noise’ term that can be deterministically controlled by a set of auxilliary variables.  The second term is small and enables us to form a linear Gaussian ‘small noise’ approximation. The decomposition allows us to take a diffusion process of interest and cast it in a form that is amenable to sampling by MCMC methods.