James Cussens

[Research] [Topics for prospective PhD students] [Projects] [Software] [Teaching] [Professional Activities] [Administration]
[Personal history] [Contact information] [Dept home page]

Research

Recent papers

• James Cussens. Bayesian network learning by compiling to weighted MAX-SAT. In Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI 2008), Helsinki, 2008.
• Vítor Santos Costa, David Page, and James Cussens. CLP(BN): Constraint logic programming for probabilistic knowledge. In Luc De Raedt, Paolo Frasconi, Kristian Kersting, and Stephen Muggleton, editors, Probabilistic Inductive Logic Programming, volume 4911 of Lectures Notes in Computer Science, pages 156-188. Springer, Berlin, 2008.
• Nicos Angelopoulos and James Cussens. Bayesian learning of Bayesian networks with informative priors. (preprint). Annals of Mathematics and Artificial Intelligence, 54(1):53-98, 2008

Full catalogue of papers

Recent talks

• A tutorial on logic-based approaches to SRL. Invited talk at ILP-MLG-SRL 09, July 2009, Leuven.
• A logical way to find high probability pedigrees, CRiSM Graphical Models and Genetic Applications workshop, University of Warwick, April 2009.
• Bayesian network learning by compiling to weighted MAX-SAT, May 2008, York.
• From correlations to networks, November 2007, York.
• Applying algebraic statistics to probabilistic-logical representations, September 2007, Leuven

Topics for prospective PhD students

My main research interests are in machine learning, particularly focusing on graphical models and inductive logic programming and combinations thereof - such a combination is known as statistical relational learning or probabilistic inductive logic programming. I also continue to have an interest in the application of machine learning techniques to natural language.

To undertake research in these areas it is necessary to have a strong mathematical background. Unfortunately, I have no specific funding available to support graduate students. Our department does award funded research studentships on a competitive basis.

Informative priors for learning graphical models

Graphical models represent relations of conditional independence between related variables, for example those between the various symptoms and causes of a disease. Automatically learning such models from data is deservedly a hot topic in machine learning. In some applications it is crucial to include information not contained in data - prior information. We have been working on methods to do this using logical representations within a Bayesian approach.

• Nicos Angelopoulos and James Cussens. Bayesian learning of Bayesian networks with informative priors. Submitted, 2006.
• Nicos Angelopoulos and James Cussens. Exploiting informative priors for Bayesian classification and regression trees. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI-05), pages 641-646, Edinburgh, 2005.

Probabilistic inductive logic programming

Inductive logic programming (ILP) uses a logical representation for machine learning. There is currently great interest in integrating probabilistic models (e.g. graphical models) into ILP. In principle there should be no barrier to doing this, since probabilistic inference is just a special case of logical (deductive) inference, but there remains considerable debate about how best to achieve this integration. One useful avenue of research is to approach this from a Bayesian point of view, since this reduces statistical inference to probabilistic inference.

• James Cussens Model equivalence of PRISM programs. In Proceedings of the Dagstuhl seminar: Probabilistic, Logical and Relational Learning - A Further Synthesis, 2007. (Associated talk given at CS Dept, KU Leuven, Belgium.)
• James Cussens. Logic-based formalisms for statistical relational learning. In Lise Getoor and Ben Taskar, editors, Introduction to Statistical Relational Learning. pages 269-290, MIT Press, Cambridge, MA, 2007.
• James Cussens. Individuals, relations and structures in probabilistic models. In Lise Getoor and David Jensen, editors, IJCAI Workshop on Learning Statistical Models from Relational Data (SRL2003), pages 32-36, Acapulco, Mexico, August 2003.
• James Cussens. Attribute-value and relational learning: A statistical viewpoint. In Luc De Raedt and Stefan Kramer, editors, Proceedings of the ICML-2000 Workshop on Attribute-Value and Relational Learning: Crossing the Boundaries, pages 35-39, 2000.

Learning Language in Logic

Many popular approaches to machine learning for natural language take a standard statistical approach, representing natural language inputs by often very large vectors of 'features'. The many successes of such an approach show the importance of using principled statistical methods. However, logical representations of language can be more compact and analysable than large feature vectors. There remains important work to be done to see to what extent the benefits of statistical and logical methods can be combined for natural language machine learning.

• James Cussens and Sašo Džeroski, editors. Learning Language in Logic, volume 1925 of LNAI. Springer, Berlin, September 2000. LNAI State-of-the-Art Survey.

Software

• gPy is a collection of Python modules for manipulating discrete hierarchical models (including Bayesian nets). It is used to support the teaching of Algorithms for Graphical Models.
• The MCMCMS (Markov chain Monte Carlo over Model Structures) system uses Stochastic Logic Programs (SLPs) to define priors for Bayesian inference. The code was written by Nicos Angelopoulos. The user guide provides a tutorial on its use.
• Pepl is an implementation of the Failure-Adjusted Maximisation (FAM) algorithm.This is an instance of the EM algorithm which produces maximum likelihood estimates for the parameters of SLPs. The code was written by Nicos Angelopoulos.
• Aaron Bate, a final year student in this department, has produced software for animating the construction of Prolog proof trees (the software draws a graphical representation of the proof tree). It uses Sicstus Prolog and Tcl/Tk. You can download the software as a gzipped tar file. I have included a simple Prolog SLP interpreter which allows you to sample from a distribution over proof trees and where each proof tree determines an acyclic digraph (the structural element of a Bayesian net). See the file slp_readme in the distribution for an explanation.

Projects

• Stochastic Logic Programs for MCMC (with Nicos Angelopoulos) 01/09/03--31/08/05. Funded by EPSRC's MathFIT programme. EPSRC's page for this project.
• APRIL II: Applications of Probabilistic Inductive Logic Programming 02/01/04--02/01/07. Funded by the EU IST programme.

Teaching

Currently, at York:

• Algorithms for Graphical Models (AGM)
• Introduction to Machine Learning (IML)
• Introduction to Programming (Python) (IPP)

Externally I've done:

• Day course on Inductive Logic Programming (21 October 2003 & 26 October 2005) for the Graduate School in Language Technology, Göteborg, Sweden
• Symbolic learning of natural language (with Dimitar Kazakov) at ESSLLI-2004 (9-21 August 2004), Nancy, France

At York, I previously taught:

• Bayesian Networks (BAN)
• Perl Programming for Biologists (PPB)
• Advanced Logic Programming (ALP)
• Introduction to Pattern Recognition & Neural Networks (IPR)

If you're interested in doing a PhD in one of my research areas, then just drop me a line. Here's details about research students.

Current PhD students

• Waleed Alsanie - Relation Extraction from Text
• Adel Aloraini - Machine learning of Bayesian networks
• Joanne Powell - PrediCtoR: Predicting the Recovery of Ancient DNA and Ancient Proteins (with Matthew Collins, Archaeology)

Former students

• Barnaby Fisher - Inductive Logic Programming and Mercury
• Heather Maclaren - Inductive Logic Programming for Software Agents: Algorithms and Implementations

Professional Activities

Programme chair

• ILP00 (with Alan Frisch)
• LLL99 LLL05 (with Claire Nédellec)

Member

• Artificial Intelligence Editorial Board
• Machine Learning Editorial Board
• SIGNLL International Advisory Committee

Invited speaker

• ILP-MLG-SRL 09
• UKKDD-2007
• AC05
• ILP04

Area chair / Senior PC

• ECML/PKDD-08
• ICML 07, ECML/PKDD-07
• ECML/PKDD-06
• ICML05

PC member

• AAAI-10, ILP 2010, ECAI-2010
• Terminologie et intelligence artificielle (TIA - 2009), ILP-09, SRL-09, IJCAI-09, ICML 09, AISTATS 09, CoNLL 09, NAACL-HLT 09, EACL Cognitive 2009, NAACL-2009 Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics
• CoNLL 08, ILP 08, ECAI 08, SBIA 08, ICML 08
• UAI07, ILP07, ACL-2007 Workshop on Cognitive Aspects of Computational Language Acquisition, TIA'07
• AAAI-06, UAI06, ILP06, SRL06, CoNLL06
• UAI05, ICML05, ECML/PKDD05, ILP05, LLLL, CoNLL05, TIA05
• ECML04 , CIFT04, UAI04 , ICML04 , SRL04 , CoNLL04 , Psycho-computational models ...
• UAI03, ICML03 , ILP03, CoNLL03 , Acquisition, apprentissage et ... , SRL2003 , ECML03
• UAI02, CIFT02, ICML02 , ECML02 , ILP02, CoNLL02
• ECML01 , CoNLL01 , ILP01 , LLL01
• CoNLL00 , ILP00 , LLL00
• ILP99 , LLL99
• ILP98

Reviewer

• EACL09, NIPS09
• NIPS08
• EACL06
• IJCAI05
• ACL04, NIPS04

Miscellaneous

• Attendee at the 2007 Dagstuhl seminar on Probabilistic, Logical and Relational Learning - A Further Synthesis
• Attendee at the 2005 Dagstuhl seminar on Probabilistic, Logical and Relational Learning - Towards a Synthesis

Administration

• Chair of the MRes Computational Biology Executive Committee
• Project Allocation Organiser

Personal history

Contact information