Photo of Sean Holden
Dr Sean Holden
University Associate Professor
of Computer Science

Sean Holden's Publications

[1]
Sean B. Holden. Machine Learning for Automated Theorem Proving: Learning to Solve SAT and QSAT. Foundations and Trends in Machine Learning. now publishers, Boston, Delft, 2021. To appear. [ bib ]
[2]
Chaitanya Mangla, Sean B. Holden, and Lawrence Paulson. Bayesian ranking for strategy scheduling in automated theorem provers. Submitted to AAAI 2022. [ bib ]
[3]
Agnieszka Slowik, Abhinav Gupta, William L. Hamilton, Mateja Jamnik, Sean B. Holden, and Christopher Pal. Exploring structural inductive biases in emergent communication. In Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, volume 43, page 3156, July 2021. arXiv:2002.01335v4. [ bib | http ]
[4]
Agnieszka Slowik, Chaitanya Mangla, Mateja Jamnik, Sean B. Holden, and Lawrence C. Paulson. Bayesian optimisation for premise selection in automated theorem proving (student abstract). In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 13919--13920. AAAI Press, February 2020. [ bib | DOI | http ]
[5]
Chaitanya Mangla, Sean B. Holden, and Lawrence Paulson. Bayesian optimisation of solver parameters in CBMC. In François Bobot and Tjark Weber, editors, Proceedings of the 18th International Workshop on Satisfiability Modulo Theories (SMT), volume 2854, pages 37--47. CEUR Workshop Proceedings, July 2020. [ bib | http ]
[6]
Abhinav Gupta, Agnieszka Slowik, William L. Hamilton, Mateja Jamnik, Sean B. Holden, and Christopher Pal. Analyzing structural priors in multi-agent communication. In Workshop on Adaptive and Learning Agents (ALA), International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), May 2020. [ bib | .pdf ]
[7]
Agnieszka Slowik, Abhinav Gupta, William L. Hamilton, Mateja Jamnik, and Sean B. Holden. Towards graph representation learning in emergent communication. In AAAI-20 Workshop on Reinforcement Learning in Games, 2020. arXiv:2001.09063v2. [ bib | .pdf ]
[8]
Agnieszka Slowik, Chaitanya Mangla, Mateja Jamnik, Sean B. Holden, and Lawrence Paulson. Bayesian optimisation for heuristic configuration in automated theorem proving. In Laura Kovács and Andrei Voronkov, editors, Vampire 2018 and Vampire 2019, The 5th and 6th Vampire Workshops, volume 71 of EPiC Series in Computing, pages 45--51. EasyChair, March 2020. [ bib | DOI | http ]
[9]
Irina M. Armean Kathryn S. Lilley, Matthew W. B. Trotter, Nicholas C. V. Pilkington, and Sean B. Holden. Co-complex protein membership evaluation using maximum entropy on go ontology and interpro annotation. Bioinformatics, 34(11):1884--1892, June 2018. [ bib | DOI | http ]
[10]
Lisa M. Breckels, Sean B. Holden, David Wojnar, Claire M. Mulvey, Andy Christoforou, Arnoud Groen, Matthew W. B. Trotter, Oliver Kohlbacher, Kathryn S. Lilley, and Laurent Gatto. Learning from heterogeneous data sources: An application in spatial proteomics. PLOS Computational Biology, 12(5):1--26, May 2016. [ bib | DOI | http ]
[11]
Sean B. Holden. HasGP: A Haskell library for Gaussian process inference. Technical Report UCAM-CL-TR-885, University of Cambridge, Computer Laboratory, April 2016. [ bib | DOI | .pdf ]
[12]
Richard Russell and Sean B. Holden. Survey propagation applied to weighted partial maximum satisfiability. Technical Report UCAM-CL-TR-883, University of Cambridge, Computer Laboratory, March 2016. [ bib | DOI | .pdf ]
[13]
K. K. Rachuri, T. Hossmann, C. Mascolo, and Sean B. Holden. Beyond location check-ins: Exploring physical and soft sensing to augment social check-in apps. In Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom), pages 123--130. IEEE, March 2015. [ bib | DOI | http ]
[14]
Ivo J. P. M. Timoteo and Sean B. Holden. Learning dynamic systems from time-series data - an application to gene regulatory networks. In Maria De Marsico, Mário Figueiredo, and Ana Fred, editors, Proceedings of the 4th International Conference on Pattern Recognition Applications and Methods (ICPRAM), volume 2, pages 324--332. SciTePress, January 2015. [ bib | DOI | http ]
[15]
James P. Bridge, Sean B. Holden, and Lawrence C. Paulson. Machine learning for first-order theorem proving. Journal of Automated Reasoning, 53(2):141--172, August 2014. [ bib | DOI | http ]
[16]
Nicholas C. V. Pilkington, Matthew W. B. Trotter, and Sean B. Holden. Multiple kernel learning for drug discovery. Molecular Informatics, 31(3-4):313--322, April 2012. [ bib | DOI | http ]
[17]
Sean B. Holden. The HasGP user manual. Technical Report UCAM-CL-TR-804, University of Cambridge, Computer Laboratory, September 2011. [ bib | DOI | .pdf ]
[18]
Richard Russell and Sean B. Holden. Handling goal utility dependencies in a satisfiability framework. In Ronen I. Brafman, Héctor Geffner, Jörg Hoffmann, and Henry Kautz, editors, Proceedings of the Twentieth International Conference on Automated Planning and Scheduling (ICAPS), pages 145--152. AAAI Press, May 2010. [ bib | http ]
[19]
Simon Fothergill, Robert Harle, and Sean B. Holden. Modeling the model athlete: Automatic coaching of rowing technique. In Niels da Vitoria Lobo, Takis Kasparis, Fabio Roli, James T. Kwok, Michael Georgiopoulos, Georgios C. Anagnostopoulos, and Marco Loog, editors, Proceedings of the Joint AIPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, volume 5342 of Lecture Notes in Computer Science, pages 372--381. Springer, Berlin, Heidelberg, December 2008. [ bib | DOI | http ]
[20]
Andrew Naish-Guzman and Sean B. Holden. Robust regression with twinned Gaussian processes. In J. C. Platt, D. Koller, Y. Singer, and S. T. Roweis, editors, Advances in Neural Information Processing Systems: Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS), volume 20, pages 1065--1072. Curran Associates, Inc., December 2007. [ bib | .pdf ]
[21]
Andrew Naish-Guzman and Sean B. Holden. The generalized FITC approximation. In J. C. Platt, D. Koller, Y. Singer, and S. T. Roweis, editors, Advances in Neural Information Processing Systems: Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS), volume 20, pages 1057--1064. Curran Associates, Inc., December 2007. [ bib | .pdf ]
[22]
Andrew Naish-Guzman, Sean B. Holden, and Ulrich Paquet. On the explicit use of example weights in the construction of classifiers. In Wlodzislaw Duch, Janusz Kacprzyk, Erkki Oja, and Slawomir Zadrożny, editors, Proceedings of the 15th International Conference on Neural Networks: Formal Models and Their Applications (ICANN), volume 3697 of Lecture Notes in Computer Science, pages 307--312. Springer, Berlin, Heidelberg, September 2005. [ bib | DOI | http ]
[23]
Ulrich Paquet, Sean B. Holden, and Andrew Naish-Guzman. Bayesian hierarchical ordinal regression. In Wlodzislaw Duch, Janusz Kacprzyk, Erkki Oja, and Slawomir Zadrożny, editors, Proceedings of the 15th International Conference on Neural Networks: Formal Models and Their Applications (ICANN), volume 3697 of Lecture Notes in Computer Science, pages 267--272. Springer, Berlin, Heidelberg, September 2005. [ bib | DOI | http ]
[24]
Peter Hammond, Tim J. Hutton, Judith E. Allanson, Linda E. Campbell, Raoul C. M. Hennekam, Sean B. Holden, Michael A. Patton, Adam Shaw, I. Karen Temple, Matthew Trotter, Kieran C. Murphy, and Robin M. Winter. 3D analysis of facial morphology. American Journal of Medical Genetics Part A, 126A(4):339--348, April 2004. [ bib | DOI | http ]
[25]
Matthew W. B. Trotter and Sean B. Holden. Support vector machines for ADME property classification. QSAR & Combinatorial Science, 22(5):533--548, July 2003. [ bib | DOI | http ]
[26]
Matthew Trotter, B. F. Buxton, and Sean B. Holden. Support vector machines in combinatorial chemistry. Measurement and Control, 34(8):235--239, October 2001. Special Feature on Signal Processing. [ bib | DOI | http ]
[27]
Robert Burbidge, Matthew Trotter, Bernard Buxton, and Sean B. Holden. STAR - sparsity through automated rejection. In José Mira and Alberto Prieto, editors, Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence: Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks (IWANN), volume 2084 of Lecture Notes in Computer Science, pages 653--660. Springer, Berlin, Heidelberg, June 2001. [ bib | http ]
[28]
R. Burbidge, M. Trotter, B. Buxton, and Sean B. Holden. Drug design by machine learning: support vector machines for pharmaceutical data analysis. Computers & Chemistry, 26(1):5--14, December 2001. Special Issue on Artificial Intelligence in Bioinformatics. [ bib | http ]
[29]
Jeevani Wickramaratna, Sean B. Holden, and Bernard F. Buxton. Performance degradation in boosting. In Josef Kittler and Fabio Roli, editors, Proceedings of the Second International Workshop on Multiple Classifier Systems (MCS), volume 2096 of Lecture Notes in Computer Science, pages 11--21. Springer, Berlin, Heidelberg, July 2001. [ bib | http ]
[30]
R. Burbidge, M. Trotter, B. Buxton, and Sean B. Holden. Drug design by machine learning: Support vector machines for pharmaceutical data analysis. In Proceedings of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB) Symposium on Artificial Intelligence in Bioinformatics, pages 1--4, April 2000. Final version in Computers & Chemistry (full details above). [ bib ]
[31]
J. Wickramaratna, Sean B. Holden, and B. Buxton. Effects of the strength of the weak learner on boosting kernel machines. In Neural Information Processing Systems (NIPS) Workshop on New Perspectives in Kernel-Based Learning Methods, 2000. No proceedings, details of published version above. [ bib ]
[32]
Sean B. Holden. Generalization. Statistics and Computing, 8(1):3--4, March 1998. Guest editorial for the Special Issue of the journal on Generalization. [ bib ]
[33]
Martin Anthony and Sean B. Holden. Cross-validation for binary classification by real-valued functions: Theoretical analysis. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory (COLT), pages 218--229. Association for Computing Machinery, July 1998. [ bib | DOI | http ]
[34]
Sean B. Holden. On algorithmic stability and the analysis of the cross-validation and holdout estimates. Technical Report RN/97/73, Department of Computer Science, University College London., 1998. [ bib ]
[35]
Sean B. Holden and Mahesan Niranjan. Average-case learning curves for radial basis function networks. Neural Computation, 9(2):441--460, February 1997. [ bib | DOI | http ]
[36]
M. Price, Sean B. Holden, and M. Sandler. Accurate parallel form filter synthesis. Electronics Letters, 32:2066--2067, October 1996. [ bib | DOI | http ]
[37]
Sean B. Holden. Cross-validation and the PAC learning model. Technical Report RN/96/64, Department of Computer Science, University College London, December 1996. [ bib ]
[38]
Sean B. Holden. PAC-like upper bounds for the sample complexity of leave-one-out cross-validation. In Proceedings of the Ninth Annual Conference on Computational Learning Theory (COLT), pages 41--50. Association for Computing Machinary, 1996. [ bib | DOI | http ]
[39]
Sean B. Holden and Mahesan Niranjan. On the practical applicability of VC dimension bounds. Neural Computation, 7(6):1265--1288, November 1995. [ bib | DOI | http ]
[40]
Sean B. Holden and Mahesan Niranjan. On the statistical physics of radial basis function networks. Neural Processing Letters, 2(4):16--19, July 1995. [ bib | DOI | http ]
[41]
Sean B. Holden and Peter J. W. Rayner. Generalization and PAC learning: Some new results for the class of generalized single-layer networks. IEEE Transactions on Neural Networks, 6(2):368--380, March 1995. [ bib | DOI | http ]
[42]
Sean B. Holden. How practical are VC dimension bounds? In Proceedings of the IEEE International Conference on Neural Networks, volume 1, pages 327--332. IEEE, 1994. [ bib | DOI | http ]
[43]
Sean B. Holden. Neural networks and the VC dimension. In Proceedings of the Third IMA International Conference on Mathematics and Signal Processing, pages 73--84. Oxford University Press, 1994. [ bib ]
[44]
Martin Anthony and Sean B. Holden. Quantifying generalization in linearly weighted neural networks. Complex Systems, 8(2):91--114, 1994. [ bib | http ]
[45]
Sean B. Holden. On the Theory of Generalization and Self-Structuring in Linearly Weighted Connectionist Networks. PhD thesis, Cambridge University Engineering Department, Trumpington Street, Cambridge CB2 1PZ, U.K, 1994. Technical Report CUED/F-INFENG/TR.161. [ bib ]
[46]
Martin Anthony and Sean B. Holden. On the power of polynomial discriminators and radial basis function networks. In Lenny Pitt, editor, Proceedings of the Sixth Annual Conference on Computational Learning Theory (COLT), pages 158--164. Association for Computing Machinery, August 1993. [ bib | DOI | http ]
[47]
Sean B. Holden. Valid generalization in radial basis function networks and modified Kanerva models. In Proceedings of the IEE Third International Conference on Artificial Neural Networks, pages 100--104. IET, May 1993. [ bib | http ]
[48]
Sean B. Holden. High order filter design and implementation project: Initial report and four month plan. Technical Report 108/SCS/93, King's College London, March 1993. [ bib ]
[49]
Sean B. Holden. High order filter design and implementation project: Summary of present position. Technical Report 108/SCS/93, King's College London, March 1993. [ bib ]
[50]
Martin Anthony and Sean B. Holden. On the power of linearly weighted neural networks. In Stan Gielen and Bert Kappen, editors, Proceedings of the International Conference on Artificial Neural Networks (ICANN), pages 738--743. Springer, Berlin, Heidelberg, September 1993. [ bib | http ]
[51]
Sean B. Holden and Peter J. W. Rayner. Generalization and learning in Volterra and radial basis function networks: A theoretical analysis. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), volume 2, pages II--273--II--276. IEEE, March 1992. [ bib | DOI | http ]
[52]
M. R. Lynch, Peter J. W. Rayner, and Sean B. Holden. Removal of degeneracy in adaptive Volterra networks by dynamic structuring. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), volume 3, pages 2069--2072. IEEE, April 1991. [ bib | DOI | http ]
[53]
M. R. Lynch, Sean B. Holden, and Peter J. W. Rayner. Complexity reduction in Volterra connectionist networks using a self-structuring LMS algorithm. In Proceedings of the IEE Second International Conference on Artificial Neural Networks, pages 44--48. IET, November 1991. [ bib | http ]

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