James Cussens

Search results

• 2022

  Algorithmic risk profiling in housing: a literature review

  Ciocanel, A., Beer, D. G., Burrows, R., Cussens, J. & Wallace, A., 1 Aug 2022, 62 p.

  Research output: Working paper

• Socio-technical imaginaries of data shaping access to housing

  Ciocanel, A., Wallace, A., Beer, D. G., Roger, B. & Cussens, J., 2022, (Unpublished).

  Research output: Contribution to conference › Abstract

• 2019

  Finding All Bayesian Network Structures within a Factor of Optimal

  Liao, Z., Sharma, C., Cussens, J. & van Beek, P., 17 Jul 2019, Proceedings of the AAAI Conference on Artificial Intelligence, 33(01). AAAI Press, p. 7892-7899 8 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 33, no. 1).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File

• Predicting persistent depressive symptoms in older adults: a machine learning approach to personalised mental healthcare

  Hatton, C., Paton, L. W., McMillan, D., Cussens, J., Gilbody, S. & Tiffin, P. A., 1 Mar 2019, In: Journal of affective disorders. 246, p. 857-860 4 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File
• 2018

  Markov Random Field MAP as Set Partitioning

  Cussens, J., 2018, Proceedings of Machine Learning Research: PGM 2018. Vol. 72. p. 85-96 12 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access
• 2017

  Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets and Complexity

  Cussens, J., Järvisalo, M., Korhonen, J. H. & Bartlett, M., 27 Jan 2017, In: Journal of Artificial Intelligence Research (JAIR). 58, p. 185-229 45 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

• Integrated testing strategies can be optimal for chemical risk classification.

  Pitchford, J. W., Cussens, J., Raseta, M. & Doe, J., Aug 2017, In: Mathematical Biosciences. 290, p. 1-8 9 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

• Polyhedral approaches to learning Bayesian networks

  Haws, D., Cussens, J. & Studený, M., 2017, Algebraic and Geometric Methods in Discrete Mathematics: Contemporary Mathematics. Providence, RI: American Mathematical Society, Vol. 685. p. 155-188 34 p.

  Research output: Chapter in Book/Report/Conference proceeding › Chapter

• Polyhedral aspects of score equivalence in Bayesian network structure learning

  Cussens, J., Haws, D. & Studený, M., Jul 2017, In: Mathematical Programming. 164, 1, p. 285-324 40 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

• Towards using the chordal graph polytope in learning decomposable models

  Studený, M. & Cussens, J., Sept 2017, In: International Journal of Approximate Reasoning. 88, p. 259-281 23 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File
• 2016

  Distributional logic programming for Bayesian knowledge representation

  Angelopoulos, N. & Cussens, J., 17 Aug 2016, (E-pub ahead of print) In: International Journal of Approximate Reasoning. 80, p. 52-66 16 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File

• The Chordal Graph Polytope for Learning Decomposable Models

  Studeny´, M. & Cussens, J., 2016, Proceedings of the Eighth International Conference on Probabilistic Graphical Models. Antonucci, A., Corani, G. & Polpo de Campos, C. (eds.). Vol. 52. p. 499-510 12 p. (Journal of Machine Learning Research: Workshop and Conference Proceedings).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  Open Access

  File
• 2015

  Exact estimation of multiple directed acyclic graphs

  Oates, C. J., Smith, J. Q., Mukherjee, S. & Cussens, J., 2015, In: Statistics and computing. p. 1-15 15 p.

  Research output: Contribution to journal › Article › peer-review

• Identification and quantification of heteroscedasticity in stock–recruitment relationships

  Panikian, G., Cussens, J. & Pitchford, J. W., Aug 2015, In: Canadian Journal of Fisheries and Aquatic Sciences. 72, 8, p. 1259-1271 13 p.

  Research output: Contribution to journal › Article › peer-review

• Integer Linear Programming for the Bayesian network structure learning problem

  Bartlett, M. & Cussens, J., 10 Mar 2015, (E-pub ahead of print) In: Artificial Intelligence. 244, p. 258-271 14 p.

  Research output: Contribution to journal › Article › peer-review

• Learning failure-free PRISM programs

  Alsanie, W. & Cussens, J., Dec 2015, In: International Journal of Approximate Reasoning. 67, p. 73-110 38 p.

  Research output: Contribution to journal › Article › peer-review

• Searching Multiregression Dynamic Models of Resting-State fMRI Networks Using Integer Programming

  Costa, L., Smith, J., Nichols, T., Cussens, J., Duff, E. P. & Makin, T. R., 1 Jun 2015, In: Bayesian Analysis. 10, 2, p. 441-478 38 p.

  Research output: Contribution to journal › Article › peer-review

  Open Access

  File
• 2014

  Improved Maximum Likelihood Reconstruction of Complex Multi-generational Pedigrees

  Sheehan, N., Bartlett, M. & Cussens, J., Nov 2014, In: Theoretical Population Biology. 97, p. 11-19 10 p.

  Research output: Contribution to journal › Article › peer-review

• Integer Programming for Bayesian Network Structure Learning

  Cussens, J., Mar 2014, In: Quality Technology and Quantitative Management. 11, 1, p. 99-110 12 p.

  Research output: Contribution to journal › Article › peer-review

• Leibniz on Probability and Statistics

  Cussens, J., 2014, (Accepted/In press) Oxford Handbook on Leibniz. Antognazza, M. R. (ed.). Oxford University Press

  Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution

• Probability, uncertainty and artificial intelligence

  Cussens, J., Nov 2014, In: Metascience. 23, 3, p. 505-511 7 p.

  Research output: Contribution to journal › Article › peer-review
• 2013

  Advances in Bayesian Network Learning using Integer Programming

  Bartlett, M. & Cussens, J., 2013, Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013). AUAI Press, p. 182-191 10 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• GOBNILP: Globally Optimal Bayesian Network learning using Integer Linear Programming

  Cussens, J. & Bartlett, M., 2013

  Research output: Non-textual form › Software

• Maximum Likelihood Pedigree Reconstruction using Integer Linear Programming

  Cussens, J., Bartlett, M., Jones, E. & Sheehan, N., Jan 2013, In: Genetic Edpidemiology. 37, 1, p. 69-83 15 p.

  Research output: Contribution to journal › Article › peer-review

• Results from an amino acid racemization inter-laboratory proficiency study: Design and performance evaluation

  Powell, J., Collins, M. J., Cussens, J., McLeod, N. & Penkman, K. E. H., Apr 2013, In: Quaternary Geochronology. 16, p. 183-197 15 p.

  Research output: Contribution to journal › Special issue › peer-review
• 2012

  An upper bound for BDeu local scores

  Cussens, J., 2012, Proc. ECAI-2012 workshop on algorithmic issues for inference in graphical models (AIGM 2012).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Column generation for exact BN learning: Work in progress

  Cussens, J., 2012, Proc. ECAI-2012 workshop on COmbining COnstraint solving with MIning and LEarning (CoCoMile 2012). p. 21-22

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Online Bayesian inference for the parameters of PRISM programs

  Cussens, J., Dec 2012, In: Machine Learning. 89, 3, p. 279-297 19 p.

  Research output: Contribution to journal › Article › peer-review
• 2011

  Approximate Bayesian computation for the parameters of PRISM programs

  Cussens, J., 2011, Proceedings of the 20th International Conference on Inductive Logic Programming. Heidelberg: Springer, Vol. 6489. p. 38-46 9 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Bayesian network learning with cutting planes

  Cussens, J., 2011, Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011). Cozman, F. & Pfeffer, A. (eds.). Corvallis, Oregon: AUAI Press, p. 153-160 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  File

• Probabilistic Instruction Cache Analysis using Bayesian Networks

  Bartlett, M., Bate, I., Cussens, J. & Kazakov, D., 2011, Proceedings of the 17th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2011). Vol. 1. p. 233 - 242

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• The Design, Evaluation and Application of Inter-laboratory Studies to Amino Acid Racemization Analysis

  Powell, J., Collins, M. J., Penkman, K. E. H., Cussens, J. & McLeod, N., 2011.

  Research output: Contribution to conference › Poster

• 2010

  Induction

  Cussens, J., 2010, Encyclopedia of Machine Learning. Sammut, C. & Webb, G. I. (eds.). Springer, p. 519-522 4 p.

  Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution

• Instruction Cache Prediction Using Bayesian Networks

  Bartlett, M., Bate, I. & Cussens, J., 1 Aug 2010, Proceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010). Lisbon, Portugal: IOS Press, Vol. 215. p. 1099-1100 2 p. (Frontiers in Artificial Intelligence and Applications; vol. 215).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Learning Bayesian Networks for Improved Instruction Cache Analysis

  Bartlett, M., Bate, I. & Cussens, J., 2010, 2010 Ninth International Conference on Machine Learning and Applications: Proceedings of the 9th International Conference on Machine Learning and Applications. IEEE, p. 417-423 7 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Searching a multivariate partition space using weighted MAX-SAT

  Liverani, S., Cussens, J. & Smith, J. Q., 2010, Proceedings of the Sixth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB) 2009. Masulli, F., Peterson, L. & Tagliaferri, R. (eds.). Springer, p. 240-253 14 p. (LNBI 6160).

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
• 2009

  Discriminative Clustering for Content-Based Tag Recommendation in Social Bookmarking Systems

  Hassan, M. T., Karim, A., Manandhar, S. & Cussens, J., 1 Sept 2009, ECML PKDD Discovery Challenge 2009 (DC09). Eisterlehner, F., Hotho, A. & Jäschke, R. (eds.). Bled, Slovenia: CEUR Workshop Proceedings, Vol. 497. p. 85-97 13 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Further Inductive Mercury Programming and IMP0.5

  Fisher, B. & Cussens, J., 1 Mar 2009.

  Research output: Working paper

• On Generative Parameterisations of Markov Logic Networks

  Cussens, J., 1 Jul 2009, Proc. SRL 09.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
• 2008

  Bayesian learning of Bayesian networks with informative priors

  Angelopoulos, N. & Cussens, J., Nov 2008, In: Annals of Mathematics and Artificial Intelligence. 54, 1-3, p. 53-98 46 p.

  Research output: Contribution to journal › Article › peer-review

• Bayesian network learning by compiling to weighted MAX-SAT

  Cussens, J., 2008, Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI 2008). McAllester, D. & Myllymaki, P. (eds.). Corvallis, Oregon: AUAI Press, p. 105-112 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

  File

• CLP(BN ): constraint logic programming for probabilistic knowledge

  Santos Costa, V., Page, D. & Cussens, J., 2008, Probabilistic Inductive Logic Programming: Theory and Applications. De Raedt, L., Frasconi, P., Kersting, K. & Muggleton, S. (eds.). Berlin: Springer, p. 156-188 32 p. (Lecture Notes in Artificial Intelligence; vol. 4911).

  Research output: Chapter in Book/Report/Conference proceeding › Chapter
• 2007

  Bayesian classification and regression trees

  Cussens, J., 2007, In: Expert Update. 9, 3, p. 37-42 6 p.

  Research output: Contribution to journal › Article › peer-review

• Inductive mercury programming

  Fisher, B. & Cussens, J., 2007, Inductive Logic Programming. Muggleton, S., Otero, R. & Tamaddoni-Nezhad, A. (eds.). BERLIN: Springer, p. 199-213 15 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

• Logic-based Formalisms for Statistical Relational Learning

  Cussens, J., 2007, Introduction to Statistical Relational Learning. Getoor, L. & Taskar, B. (eds.). Cambridge, MA: MIT Press

  Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution

• Model Equivalence of PRISM programs

  Cussens, J., 2007, Proceedings of the Dagstuhl seminar: Probabilistic, Logical and Relational Learning - A Further Synthesis.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
• 2006

  Inductive Mercury Programming

  Fisher, B. & Cussens, J., 2006, p. 199-213.

  Research output: Contribution to conference › Paper › peer-review
• 2005

  Deductive Reasoning and Statistical Inference

  Cussens, J., 2005, Encyclopedia of Statistics in Behavioral Science. Wiley-Blackwell

  Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution

• Exploiting independence for branch operations in Bayesian learning of CRTs

  Angelopoulos, N. & Cussens, J., 2005.

  Research output: Contribution to conference › Paper › peer-review

• Exploiting informative priors for Bayesian classification and regression trees

  Angelopoulos, N., Cussens, J., Pack Kaelbling, L. (ed.) & Saffiotti, A. (ed.), 2005, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Professional Book Center, p. 641-646 5 p.

  Research output: Chapter in Book/Report/Conference proceeding › Conference contribution