James Cussens

[Research] [PhD supervision] [Projects] [Software] [Teaching] [Professional Activities] [Administration]
[Personal history] [Contact information] [Dept home page]

Research

Google scholar profile

Recent papers

• James Cussens. Online Bayesian inference for the parameters of PRISM programs. Machine Learning. (to appear) (Preprint available)
• James Cussens. Bayesian network learning with cutting planes (PDF). In Fabio G. Cozman and Avi Pfeffer, editors, Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), pages 153-160, Barcelona, 2011. AUAI Press.
• James Cussens. Approximate Bayesian computation for the parameters of PRISM programs. (slides) In P. Frasconi and F.A. Lisi (Eds.): ILP 2010, LNAI 6489, pp. 38--46. Springer, Heidelberg (2011).
• James Cussens. Maximum likelihood pedigree reconstruction using integer programming. In Proceedings of the Workshop on Constraint Based Methods for Bioinformatics (WCB-10), Edinburgh, July 2010.

Full catalogue of papers

Recent talks

• Bayesian network learning using cutting planes, Gatsby Institute, 23 March 2011.
• Finding the most probable pedigree using integer programming, City University, 2 June 2010.

Artificial Intelligence at York

PhD supervision

Current PhD students

• Garo Panikian - Learning probabilistic graphical models of complex biological systems
• Eman Aljohani - Informative priors for learning graphical models
• Waleed Alsanie - Learning probabilistic logic programs for statistical relational learning
• Joanne Powell - PrediCtoR: Predicting the Recovery of Ancient DNA and Ancient Proteins (with Matthew Collins, Archaeology)

Former students

• Adel Aloraini - Extending the graphical represetation of KEGG pathways for a better understanding of prostate cancer using machine learning
• Barnaby Fisher - Inductive Logic Programming and Mercury (MSc by Research)
• Heather Maclaren - Inductive Logic Programming for Software Agents: Algorithms and Implementations

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. Annals of Mathematics and Artificial Intelligence, 54(1):53-98, 2008.
• 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.

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

• A graphical model approach to pedigree construction using constrained optimisation 03/10/11-03/10/14. Funded by the MRC.
• 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 Artificial Intelligence (ARIN)
• Introduction to Machine Learning (IML)

Professional Activities

Programme chair

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

Editorial duties

• Artificial Intelligence Associate Editor
• Machine Learning Action Editor

Member

• SIGNLL International Advisory Committee

Invited speaker

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

Area chair/Senior PC

• ECAI-12
• IJCAI-11, AAAI-11
• ECML/PKDD-08
• ECML/PKDD-07
• ECML/PKDD-06
• ICML05

Co-organiser

• ICML 2012 Workshop on Statistical Relational Learning (SRL-12) together with Kristian Kersting, Sriraam Natarajan, and David Poole.

PC member

• StaRAI-12, UAI 2012, CoCoMile 2012, ACL 2012, Cognitive 2012, ICML 2012, ILP 2012, AAAI-12, ECML/PKDD-12, KR 2012
• ECML/PKDD-11, UAI 2011, ILP 2011, ICML 2011
• AAAI-10, ILP 2010, ECAI-2010, ECML/PKDD 2010, SBIA 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
• ICML 07, 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

• NIPS12
• EACL09, NIPS09
• NIPS08
• EACL06
• IJCAI05
• ACL04, NIPS04

Miscellaneous

• Attendee at the 2011 Dagstuhl seminar on Constraint Programming meets Machine Learning and Data Mining
• 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
• Student Projects Organiser

Personal history

Contact information