Alex Davies
now a Software Engineer, Google DeepMind
Publications
Effective implementation of Gaussian process regression for machine learning
Alex Davies, 2015. University of Cambridge, Department of Engineering, Cambridge, UK.
Abstract▼ URL
This thesis presents frameworks for the effective implementation of Gaussian process regression for machine learning. It addresses this in three parts: effective iterative methods for calculating the predictive distribution and derivatives of a Gaussian process with fixed hyper-parameters, defining three broad classes of kernels of controllable complexity that allow for an order of magnitude scaling in the previous framework and an investigation into alternative objective functions and improved derivatives for the optimization of model hyper-parameters.
Language-independent Bayesian sentiment mining of Twitter
A. Davies, Z. Ghahramani, August 2011. (In In The Fifth Workshop on Social Network Mining and Analysis (SNA-KDD 2011)).
Abstract▼ URL
This paper outlines a new language-independent model for sentiment analysis of short, social-network statuses. We demonstrate this on data from Twitter, modelling happy vs sad sentiment, and show that in some circumstances this outperforms similar Naive Bayes models by more than 10%. We also propose an extension to allow the modelling of differ- ent sentiment distributions in different geographic regions, while incorporating information from neighbouring regions. We outline the considerations when creating a system analysing Twitter data and present a scalable system of data acquisi- tion and prediction that can monitor the sentiment of tweets in real time.
The Random Forest Kernel and other kernels for big data from random partitions
Alex Davies, Zoubin Ghahramani, 2014. (arXiv).
Abstract▼ URL
We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing O(N) inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.
SIGMa: simple greedy matching for aligning large knowledge bases
Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani, 2013. (In KDD). Association for Computing Machinery. ISBN: 978-1-4503-2174-7.
Abstract▼ URL
The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically aligning these knowledge bases would make it possible to unify many sources of structured knowledge and answer complex queries. However, the efficient alignment of large-scale knowledge bases still poses a considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts. SiGMa is an iterative propagation algorithm which leverages both the structural information from the relationship graph as well as flexible similarity measures between entity properties in a greedy local search, thus making it scalable. Despite its greedy nature, our experiments indicate that SiGMa can efficiently match some of the world’s largest knowledge bases with high precision. We provide additional experiments on benchmark datasets which demonstrate that SiGMa can outperform state-of-the-art approaches both in accuracy and efficiency.