LFM Project


Machine learning has had wide success in application over the last decade, with significant contributions in classification, probabilistic modelling and analysis of structured data. Over the next decade we expect significant progress in applying these machine learning models to very large data sets (on the order of millions or billions of examples, e.g. web based corpora). Improving the scalability of machine learning methods is a very active area of research (see, e.g., this book). We think of these methods as large data learning. These methods will prove to be very valuable, but in this work we will be focussing on the other end of the spectrum. In particular, we will be looking at small data learning, by which we mean data sets which are small relative to the complexity of the system from which they are derived. Examples of this type of data include:

These data are representative of a growing number of application areas where the number of features is large (high dimensional data) and the number of data points is relatively small. Additionally, for example in the case of the medical and computational health applications, there may be many additional variables that effect the system but are unobserved.

This type of data is sometimes known in statistics as large p small n, where p represents the dimensionality of the feature space and n the number of data points.In this domain there are two key problems with purely data driven approaches. Firstly, if data is scarce relative to the complexity of the model we may be unable to make accurate predictions on test data. The second problem also applies for larger n. When a model is forced to extrapolate, i.e. make predictions in a regime in which data has not yet been seen, the absence of a sensible physical foundation for a system can lead to poor performance. One major advantage of dealing with a physically well characterised system is that it enables accurate prediction even in regions where there may be no available training data, for example Pioneer space probes have been able to enter different extra terrestrial orbits despite the absence of data for these orbits.}

Turning to a purely mechanistic approach does leaves us with a different set of problems. In particular, accurate description of a complex system through a mechanistic modelling paradigm such as differential equations can lead to an extremely complex model. Identifying and specifying all the interactions may not be feasible and we are still faced with the problem of how to parameterise the system.

In this project we advocate an alternative approach. We will augment traditional machine learning approaches with mechanistic systems models. Our main algorithmic tool will be the Gaussian process, and the focus of our mechanistic models will be differential equations. We will refer to the resulting models as convolution processes. Rather than relying on an exclusively mechanistic or data driven approach we propose a hybrid system which involves a (typically overly simplistic) mechanistic model of the system which can easily be augmented through machine learning techniques.

Progress so far is:

The project is sponsored by Google Faculty Research Award Project Ref Machine Learning 2008 and is a collaboration with Dr David Luengo of Carlos III University in Madrid.

Personnel from ML@SITraN


The following software has been made available either wholly or partly as a result of work on this project:


The following conference publications were made associated with this project.

M. A. Álvarez, D. Luengo and N. D. Lawrence. (2009) “Latent force models” in D. van Dyk and M. Welling (eds) Proceedings of the Twelfth International Workshop on Artificial Intelligence and Statistics, JMLR W&CP 5, Clearwater Beach, FL, pp 9–16. [Software][PDF][Google Scholar Search]


Purely data driven approaches for machine learning present difficulties when data is scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data driven modeling with a physical model of the system. We show how different, physically-inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from computational biology, motion capture and geostatistics.

M. A. Álvarez and N. D. Lawrence. (2009) “Sparse convolved Gaussian processes for multi-output regression” in D. Koller, D. Schuurmans, Y. Bengio and L. Bottou (eds) NIPS, MIT Press, Cambridge, MA, pp 57–64. [Software][PDF][Google Scholar Search]


We present a sparse approximation approach for dependent output Gaussian processes (GP). Employing a latent function framework, we apply the convolution process formalism to establish dependencies between output variables, where each latent function is represented as a GP. Based on these latent functions, we establish an approximation scheme using a conditional independence assumption between the output processes, leading to an approximation of the full covariance which is determined by the locations at which the latent functions are evaluated. We show results of the proposed methodology for synthetic data and real world applications on pollution prediction and a sensor network.

The following publications have provided background to our work in this project.