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2020 – today
- 2023
[c46]James Cussens:
Branch-Price-and-Cut for Causal Discovery. CLeaR 2023: 642-661- 2022
- [e6]James Cussens, Kun Zhang:
Uncertainty in Artificial Intelligence, Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, UAI 2022, 1-5 August 2022, Eindhoven, The Netherlands. Proceedings of Machine Learning Research 180, PMLR 2022 [contents] - 2021
- [j16]Milan Studený
, James Cussens, Václav Kratochvíl:
The dual polyhedron to the chordal graph polytope and the rebuttal of the chordal graph conjecture. Int. J. Approx. Reason. 138: 188-203 (2021) - 2020
- [c45]Alvaro Henrique Chaim Correia, James Cussens, Cassio P. de Campos:
On Pruning for Score-Based Bayesian Network Structure Learning. AISTATS 2020: 2709-2718 - [c44]Teny Handhayani, James Cussens:
Kernel-based Approach for Learning Causal Graphs from Mixed Data. PGM 2020: 221-232 - [c43]Charupriya Sharma, Zhenyu A. Liao, James Cussens, Peter van Beek:
A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations. PGM 2020: 413-424 - [c42]Milan Studený, James Cussens, Václav Kratochvíl:
Dual Formulation of the Chordal Graph Conjecture. PGM 2020: 449-460 - [c41]James Cussens:
GOBNILP: Learning Bayesian network structure with integer programming. PGM 2020: 605-608 - [i18]Zhenyu A. Liao, Charupriya Sharma, James Cussens, Peter van Beek:
Learning All Credible Bayesian Network Structures for Model Averaging. CoRR abs/2008.13618 (2020) - [i17]Charupriya Sharma, Zhenyu A. Liao, James Cussens, Peter van Beek:
A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations. CoRR abs/2011.01444 (2020)
2010 – 2019
- 2019
- [c40]Zhenyu A. Liao, Charupriya Sharma, James Cussens, Peter van Beek:
Finding All Bayesian Network Structures within a Factor of Optimal. AAAI 2019: 7892-7899 - [c39]Durdane Kocacoban, James Cussens:
Online Causal Structure Learning in the Presence of Latent Variables. ICMLA 2019: 392-395 - [i16]Durdane Kocacoban, James Cussens:
Online Causal Structure Learning in the Presence of Latent Variables. CoRR abs/1904.13247 (2019) - [i15]Alvaro H. C. Correia, James Cussens, Cassio P. de Campos:
On Pruning for Score-Based Bayesian Network Structure Learning. CoRR abs/1905.09943 (2019) - [i14]Teny Handhayani, James Cussens:
Kernel-based Approach to Handle Mixed Data for Inferring Causal Graphs. CoRR abs/1910.03055 (2019) - 2018
- [j15]Arjen Hommersom, James Cussens:
Probabilistic logic programming (PLP) 2016. Int. J. Approx. Reason. 96: 56 (2018) - [j14]James Cussens, Alessandra Russo:
Preface to the special issue on inductive logic programming. Mach. Learn. 107(7): 1095-1096 (2018) - [c38]James Cussens:
Markov Random Field MAP as Set Partitioning. PGM 2018: 85-96 - [i13]James Cussens:
Finding Minimal Cost Herbrand Models with Branch-Cut-and-Price. CoRR abs/1808.04758 (2018) - [i12]Zhenyu A. Liao, Charupriya Sharma, James Cussens, Peter van Beek:
Finding All Bayesian Network Structures within a Factor of Optimal. CoRR abs/1811.05039 (2018) - 2017
- [j13]Mark Bartlett, James Cussens:
Integer Linear Programming for the Bayesian network structure learning problem. Artif. Intell. 244: 258-271 (2017) - [j12]Nicos Angelopoulos, James Cussens:
Distributional logic programming for Bayesian knowledge representation. Int. J. Approx. Reason. 80: 52-66 (2017) - [j11]Milan Studený, James Cussens:
Towards using the chordal graph polytope in learning decomposable models. Int. J. Approx. Reason. 88: 259-281 (2017) - [j10]James Cussens, Matti Järvisalo, Janne H. Korhonen, Mark Bartlett:
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets and Complexity. J. Artif. Intell. Res. 58: 185-229 (2017) - [j9]James Cussens, David Haws, Milan Studený:
Polyhedral aspects of score equivalence in Bayesian network structure learning. Math. Program. 164(1-2): 285-324 (2017) - [c37]James Cussens, Matti Järvisalo, Janne H. Korhonen, Mark Bartlett:
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets and Complexity (Extended Abstract). IJCAI 2017: 4990-4994 - [e5]James Cussens, Alessandra Russo:
Proceedings of the 26th International Conference on Inductive Logic Programming (Short papers), London, UK, 2016. CEUR Workshop Proceedings 1865, CEUR-WS.org 2017 [contents] - [e4]James Cussens, Alessandra Russo:
Inductive Logic Programming - 26th International Conference, ILP 2016, London, UK, September 4-6, 2016, Revised Selected Papers. Lecture Notes in Computer Science 10326, Springer 2017, ISBN 978-3-319-63341-1 [contents] - [r2]James Cussens:
Induction. Encyclopedia of Machine Learning and Data Mining 2017: 637-640 - 2016
- [j8]Chris J. Oates, Jim Q. Smith, Sach Mukherjee, James Cussens:
Exact estimation of multiple directed acyclic graphs. Stat. Comput. 26(4): 797-811 (2016) - [c36]Milan Studený, James Cussens:
The Chordal Graph Polytope for Learning Decomposable Models. Probabilistic Graphical Models 2016: 499-510 - [i11]James Cussens, Matti Järvisalo, Janne H. Korhonen, Mark Bartlett:
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets, and Complexity. CoRR abs/1605.04071 (2016) - 2015
- [j7]Waleed Alsanie, James Cussens:
Learning failure-free PRISM programs. Int. J. Approx. Reason. 67: 73-110 (2015) - [j6]James Cussens, Luc De Raedt, Angelika Kimmig, Taisuke Sato:
Introduction to the special issue on probability, logic and learning. Theory Pract. Log. Program. 15(2): 145-146 (2015) - [i10]James Cussens:
First-order integer programming for MAP problems. CoRR abs/1507.02912 (2015) - 2013
- [c35]James Cussens, Mark Bartlett:
Advances in Bayesian Network Learning using Integer Programming. UAI 2013 - [i9]Nicos Angelopoulos, James Cussens:
Markov Chain Monte Carlo using Tree-Based Priors on Model Structure. CoRR abs/1301.2254 (2013) - [i8]James Cussens:
Stochastic Logic Programs: Sampling, Inference and Applications. CoRR abs/1301.3846 (2013) - [i7]James Cussens:
Loglinear models for first-order probabilistic reasoning. CoRR abs/1301.6687 (2013) - [i6]James Cussens, Mark Bartlett:
Advances in Bayesian Network Learning using Integer Programming. CoRR abs/1309.6825 (2013) - 2012
- [j5]James Cussens:
Online Bayesian inference for the parameters of PRISM programs. Mach. Learn. 89(3): 279-297 (2012) - [i5]James Cussens:
Bayesian network learning with cutting planes. CoRR abs/1202.3713 (2012) - [i4]James Cussens:
Bayesian network learning by compiling to weighted MAX-SAT. CoRR abs/1206.3244 (2012) - [i3]Vítor Santos Costa, David Page, Maleeha Qazi, James Cussens:
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. CoRR abs/1212.2519 (2012) - 2011
- [c34]James Cussens:
Online Bayesian Inference for the Parameters of PRISM Programs. ILP 2011: 14-19 - [c33]Waleed Alsanie, James Cussens:
Learning a Generative Failure-Free PRISM Clause. ILP (Late Breaking Papers) 2011: 87-93 - [c32]Mark Bartlett, Iain Bate, James Cussens, Dimitar Kazakov:
Probabilistic Instruction Cache Analysis Using Bayesian Networks. RTCSA (1) 2011: 233-242 - [c31]James Cussens:
Bayesian network learning with cutting planes. UAI 2011: 153-160 - 2010
- [c30]Mark Bartlett, Iain Bate, James Cussens:
Instruction Cache Prediction Using Bayesian Networks. ECAI 2010: 1099-1100 - [c29]James Cussens:
Maximum likelihood pedigree reconstruction using integer programming. WCB@ICLP 2010: 8-19 - [c28]Mark Bartlett, Iain Bate, James Cussens:
Learning Bayesian Networks for Improved Instruction Cache Analysis. ICMLA 2010: 417-423 - [c27]James Cussens:
Approximate Bayesian Computation for the Parameters of PRISM Programs. ILP 2010: 38-46 - [r1]James Cussens:
Induction. Encyclopedia of Machine Learning 2010: 519-522
2000 – 2009
- 2009
- [c26]Silvia Liverani, James Cussens, Jim Q. Smith:
Searching a Multivariate Partition Space Using MAX-SAT. CIBB 2009: 240-253 - [c25]Malik Tahir Hassan, Asim Karim, Suresh Manandhar, James Cussens:
Discriminative Clustering for Content-Based Tag Recommendation in Social Bookmarking Systems. DC@PKDD/ECML 2009 - 2008
- [j4]Nicos Angelopoulos, James Cussens:
Bayesian learning of Bayesian networks with informative priors. Ann. Math. Artif. Intell. 54(1-3): 53-98 (2008) - [c24]James Cussens:
Bayesian network learning by compiling to weighted MAX-SAT. UAI 2008: 105-112 - [p1]Vítor Santos Costa, David Page, James Cussens:
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. Probabilistic Inductive Logic Programming 2008: 156-188 - 2007
- [i2]James Cussens:
Model equivalence of PRISM programs. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 - 2006
- [c23]Barnaby Fisher, James Cussens:
Inductive Mercury Programming. ILP 2006: 199-213 - 2005
- [c22]Nicos Angelopoulos, James Cussens:
Tempering for Bayesian C&RT. ICML 2005: 17-24 - [c21]Nicos Angelopoulos, James Cussens:
Exploiting Informative Priors for Bayesian Classification and Regression Trees. IJCAI 2005: 641-646 - [i1]Nicos Angelopoulos, James Cussens:
Exploiting independence for branch operations in Bayesian learning of C&RTs. Probabilistic, Logical and Relational Learning 2005 - 2004
- [c20]James Cussens:
At the Interface of Inductive Logic Programming and Statistics. ILP 2004: 2-3 - 2003
- [c19]Vítor Santos Costa, David Page, Maleeha Qazi, James Cussens:
CLP(BN): Constraint Logic Programming for Probabilistic Knowledge. UAI 2003: 517-524 - 2002
- [c18]James Cussens:
Issues in Learning Language in Logic. Computational Logic: Logic Programming and Beyond 2002: 491-505 - 2001
- [j3]James Cussens:
Parameter Estimation in Stochastic Logic Programs. Mach. Learn. 44(3): 245-271 (2001) - [c17]James Cussens:
Statistical Aspects of Stochastic Logic Programs. AISTATS 2001: 77-82 - [c16]Nicos Angelopoulos, James Cussens:
Prolog Issues and Experimental Results of an MCMC Algorithm. INAP (LNCS Volume) 2001: 186-196 - [c15]Nicos Angelopoulos, James Cussens:
Prolog Issues of an MCMC Algorithm. INAP 2001: 246-253 - [c14]Nicos Angelopoulos, James Cussens:
Markov Chain Monte Carlo using Tree-Based Priors on Model Structure. UAI 2001: 16-23 - 2000
- [c13]James Cussens, Stephen Pulman:
Incorporating Linguistics Constraints into Inductive Logic Programming. CoNLL/LLL 2000: 184-193 - [c12]James Cussens:
Stochastic Logic Programs: Sampling, Inference and Applications. UAI 2000: 115-122 - [e3]James Cussens, Alan M. Frisch:
Inductive Logic Programming, 10th International Conference, ILP 2000, Work-in-progress reports, London, UK, July 2000, Proceedings. CEUR Workshop Proceedings 35, CEUR-WS.org 2000 [contents] - [e2]James Cussens, Alan M. Frisch:
Inductive Logic Programming, 10th International Conference, ILP 2000, London, UK, July 24-27, 2000, Proceedings. Lecture Notes in Computer Science 1866, Springer 2000, ISBN 3-540-67795-X [contents] - [e1]James Cussens, Saso Dzeroski:
Learning Language in Logic. Lecture Notes in Computer Science 1925, Springer 2000, ISBN 3-540-41145-3 [contents]
1990 – 1999
- 1999
- [j2]James Cussens:
Integrating Probabilistic and Logical Reasoning. Electron. Trans. Artif. Intell. 3(B): 79-103 (1999) - [c11]James Cussens, Saso Dzeroski, Tomaz Erjavec:
Morphosyntactic Tagging of Slovene Using Progol. ILP 1999: 68-79 - [c10]Saso Dzeroski, James Cussens, Suresh Manandhar:
An Introduction to Inductive Logic Programming and Learning Language in Logic. Learning Language in Logic 1999: 3-35 - [c9]James Cussens, Stephen G. Pulman:
Experiments in Inductive Chart Parsing. Learning Language in Logic 1999: 143-156 - [c8]James Cussens:
Loglinear models for first-order probabilistic reasoning. UAI 1999: 126-133 - 1998
- [c7]James Cussens:
Using Prior Probabilities and Density Estimation for Relational Classification. ILP 1998: 106-115 - 1997
- [c6]James Cussens:
Part-of-Speech Tagging Using Progol. ILP 1997: 93-108 - 1996
- [j1]James Cussens:
Deduction, induction and probabilistic support. Synth. 108(1): 1-10 (1996) - 1995
- [c5]James Cussens:
A Bayesian Analysis of Algorithms for Learning Finite Functions. ICML 1995: 142-149 - 1993
- [c4]James Cussens, Anthony Hunter, Ashwin Srinivasan:
Generating Explicit Orderings for Non-monotonic Logics. AAAI 1993: 420-425 - [c3]James Cussens:
Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability. ECML 1993: 136-152 - 1992
- [c2]James Cussens, Anthony Hunter:
Using Maximum Entropy in a Defeasible Logic with Probabilistic Semantics. IPMU 1992: 43-52 - 1991
- [c1]James Cussens, Anthony Hunter:
Using Defeasible Logic for a Window on a Probabilistic Database: Some Preliminary Notes. ECSQARU 1991: 146-152
Coauthor Index
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