Machine Learning
Machine Learning research at York focuses on statistical
relational learning, reinforcement
learning, Bayesian networks and other
graphical models and natural language learning.
Bayesian networks and other graphical models
Graphical models use graphs to represent probabilistic
interactions between variables modelling a given
situation. The most popular sort of graphical model is a
Bayesian network which uses a directed graph; in certain
circumstances Bayesian networks can represent causal
relations between variables. A key problem is to how to
get hold of a graphical model in the first
place. Machine learning offers a solution where
graphical models are inferred from data drawn from the
situation being modelled. For this task we have
developed the
GOBNILP software for exact Bayesian network learning.
Recent publications in Bayesian network and other graphical models
-
Nuala Sheehan, Mark Bartlett and James Cussens.
Improved Maximum Likelihood Reconstruction of
Complex Multi-generational Pedigrees. Theoretical
Population Biology, 97:11-19, 2014.
- Chris J. Oates, Jim Q. Smith, Sach Mukherjee and James
Cussens. Exact Estimation of
Multiple Directed Acyclic Graphs. Arkiv 1404.1238,
April 2014.
- Lilia Costa, Jim
Smith, Thomas Nichols, James Cussens, Eugene P. Duff and
Tamar R. Makin.
Searching Multiregression Dynamic Models of Resting-State fMRI
Networks using Integer Programming. Bayesian
Analysis. Posted online 2014-10-28.
-
James Cussens. Integer Programming for Bayesian
Network Structure Learning. Quality
Technology and Quantitative Management,
11(1):99-110,
March 2014.
-
Mark Barlett and James Cussens.
Advances in Bayesian Network Learning using Integer
Programming.
Proceedings of the 29th Conference on Uncertainty in
Artificial Intelligence (UAI 2013). 182-191, 2013.
- James Cussens, Mark Bartlett, Elinor M. Jones and Nuala
A. Sheehan.
Maximum Likelihood Pedigree Reconstruction using
Integer Linear Programming. Genetic Epidemiology, 37(1):69-83, Janary 2013.
-
James Cussens. Bayesian network learning with cutting
planes.
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.
- Mark Bartlett, Iain Bate, James Cussens and Dimitar Kazakov.
Probabilistic Instruction Cache Analysis using Bayesian Networks.
Proceedings of the 17th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2011)
- Mark Bartlett, Iain Bate, and James Cussens.
Instruction
cache prediction using Bayesian networks.
In Proc. ECAI 2010 - 19th European Conference on Artificial
Intelligence, pages 1099-1100, 2010.
Short paper.
-
Mark Bartlett, Iain Bate, and James Cussens.
Learning
Bayesian networks for improved instruction cache analysis.
In Proceedings of the Ninth International Conference on Machine Learning
and Applications (ICMLA 2010), pages 417-423. IEEE, 2010.
-
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.
-
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.
-
Nicos Angelopoulos and James Cussens. Bayesian learning of Bayesian networks with informative priors. Annals of Mathematics and Artificial Intelligence, 54(1):53-98,
2008.
Research topics in Bayesian network and other graphical models
Graphical model learning using constrained optimisation algorithms
A common approach to graphical model learning is to define a score
measuring how well any given graphical model is
supported by observed data. The problem then reduces to
finding a graphical model with the highest
score. Finding such a model is known to be an NP-hard
problem. This motivates using optimisation approaches
such as integer linear programming or weighted MAX-SAT
solvers to do the searching. An MRC project A
graphical model approach to pedigree construction using
constrained optimisation started Oct 2011. This is a
joint project with Leicester and Bristol which uses
integer linear programming to induce Bayesian networks
representing 'pedigrees' (family trees).
-
Nuala Sheehan, Mark Bartlett and James Cussens.
Improved Maximum Likelihood Reconstruction of
Complex Multi-generational Pedigrees. Theoretical
Population Biology, 97:11-19, 2014.
- Chris J. Oates, Jim Q. Smith, Sach Mukherjee and James
Cussens. Exact Estimation of
Multiple Directed Acyclic Graphs. Arkiv 1404.1238,
April 2014.
- Lilia Costa, Jim
Smith, Thomas Nichols, James Cussens, Eugene P. Duff and
Tamar R. Makin.
Searching Multiregression Dynamic Models of Resting-State fMRI
Networks using Integer Programming. Bayesian
Analysis. Posted online 2014-10-28.
-
James Cussens. Integer Programming for Bayesian
Network Structure Learning. Quality
Technology and Quantitative Management,
11(1):99-110,
March 2014.
-
Mark Barlett and James Cussens.
Advances in Bayesian Network Learning using Integer
Programming.
Proceedings of the 29th Conference on Uncertainty in
Artificial Intelligence (UAI 2013). 182-191, 2013.
- James Cussens, Mark Bartlett, Elinor M. Jones and Nuala
A. Sheehan.
Maximum Likelihood Pedigree Reconstruction using
Integer Linear Programming. Genetic Epidemiology, 37(1):69-83, Janary 2013.
-
James Cussens. Bayesian network learning with cutting
planes.
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.
Maximum
likelihood pedigree reconstruction using integer programming.
In Proceedings of the Workshop on Constraint Based Methods for
Bioinformatics (WCB-10), Edinburgh, July 2010.
-
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.
Graphical models for worst-case execution time
In collaborative with the Real-time systems group, we have been
learning BNs to model worst-case execution times. A particular
focus has been on modelling cache hits and misses.
- Mark Bartlett, Iain Bate, James Cussens and Dimitar Kazakov.
Probabilistic Instruction Cache Analysis using Bayesian Networks.
Proceedings of the 17th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2011)
- Mark Bartlett, Iain Bate, and James Cussens.
Instruction
cache prediction using Bayesian networks.
In Proc. ECAI 2010 - 19th European Conference on Artificial
Intelligence, pages 1099-1100, 2010.
Short paper.
-
Mark Bartlett, Iain Bate, and James Cussens.
Learning
Bayesian networks for improved instruction cache analysis.
In Proceedings of the Ninth International Conference on Machine Learning
and Applications (ICMLA 2010), pages 417-423. IEEE, 2010.
Students working on Bayesian networks and other graphical models
Statistical relational learning
Statistical relational learning (SRL) is a branch of machine
learning which aims to combine the strengths of probabilistic
and relational representations. At York the focus is on
first-order logic to represent relations, and so our
brand of SRL is probabilistic inductive logic programming (PILP).
Despite the current great interest in SRL, 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.
Publications in SRL
-
James Cussens. Approximate Bayesian computation for the parameters of
PRISM programs. In P. Frasconi and F.A. Lisi (Eds.): ILP
2010, LNAI 6489, pp. 38--46. Springer, Heidelberg (2011).
-
James Cussens.
On
generative parameterisations of Markov logic networks.
In Proc. SRL 09, Leuven, July 2009.
-
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.
-
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.
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.
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.
Research topics in SRL
Using algebraic statistics to analyse conditional
independence in SRL formalisms
Graphical models such as Bayesian networks represent relations
of conditional independence via the structure of a graph. SRL
formalisms, such as the PRISM
system, are capable of representing more complex conditional
independence relations, and so graphs aren't enough. One
promising avenue is to use algebraic statistics to analyse
these more complex relations. Algebraic statistics is a
sub-branch of algebraic geometry, so a nice bonus is the
geometrical intuitions which it provides.
Fast implementations for SRL
SRL is a computationally demanding form of machine learning
requiring both symbolic and numerical computation. An
interesting route would be to implement SRL algorithms in the Mercury
language. Roughly speaking, Mercury is strongly-typed
Prolog which compiles to C (and thus to native
code). Encouraging results have already been achieved for
non-probabilistic ILP.
Reinforcement learning
Reinforcement learning (RL) is a highly popular machine learning
technique, mainly due to its natural fit to the agent paradigm
(i.e. learning by repeatedly acting and sensing in an environment) and
its resulting wide application potential.Despite these advantages, RL
suffers from scalability problems which have prevented its successful
use in many complex real-world domains. Our research is focused on
Knowledge-Based Reinforcement learning, which is concerned with the
use of domain knowledge to scale-up and improve reinforcement learning
and support transfer learning. Conversely, reinforcement learning is
also used to revise domain knowledge. Our research considers both
single-agent and multi-agent learning.
Our application areas include mobile sensor management, robotics, and
network intrusion detection and prevention. The research has been
partially sponsored by QinetiQ and the MoD.
- S. Devlin, M. Grzes, and D. Kudenko (2011): An Empirical Study
of Potential-Based Reward Shaping and Advice in Complex, Multi-Agent
Systems. In Advances in Complex Systems (ACS) 14(2).
- S. Devlin and D. Kudenko (2011): "Theoretical Considerations of
Potential-Based Reward Shaping for Multi-Agent Systems", Tenth
International Conference on Autonomous Agents and Multiagent Systems
(AAMAS).
- M. Grzes, D. Kudenko (2010): "PAC-MDP Learning with Knowledge-based Admissible Models", Ninth International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS).
- M. Grzes, D. Kudenko (2010): "Online Learning of Shaping Rewards in Reinforcement Learning", Neural Networks, 23(4).
- M. Grzes and D. Kudenko (2009). “Reinforcement Learning with Reward Shaping and Mixed Resolution Function Approximation”. International Journal of Agent Technologies and Systems (IJATS), 1(2):36-54.
- M. Grzes, D. Kudenko (2008): "Plan-based reward shaping for reinforcement learning", Fourth IEEE International Conference on Intelligent Systems (IS).
Students working on Reinforcement Learning
Natural language learning
Please see our page on natural language processing
for information on machine learning of natural
language.