Sean B. Holden - researchr alias

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Viewing Publication 1 - 36 from 36

2025

Partition Tree Ensembles for Improving Multi-Class ClassificationMiran Özdogan, Alan Jeffares, Sean B. Holden. icpram 2025: 55-69 [doi]

2024

MUGI-MRI: Enhancing Breast Cancer Classification through Multiplex Graph Neural Networks in DCE-MRIFrancesco Ceccarelli, Francesco Prinzi, Pietro Liò, Salvatore Vitabile, Sean B. Holden. ijcnn 2024: 1-8 [doi]

Integrating Structure and Sequence: Protein Graph Embeddings via GNNs and LLMsFrancesco Ceccarelli, Lorenzo Giusti, Sean B. Holden, Pietro Liò. icpram 2024: 582-593 [doi]

2023

Connect++: A New Automated Theorem Prover Based on the Connection CalculusSean B. Holden. arecca 2024: 95-106 [doi]

A Syntax for Connection ProofsJens Otten, Sean B. Holden. arecca 2024: 84-94 [doi]

Connections: Markov Decision Processes for Classical, Intuitionistic and Modal Connection CalculiFredrik Rømming, Jens Otten, Sean B. Holden. arecca 2024: 107-118 [doi]

2022

Bayesian Ranking for Strategy Scheduling in Automated Theorem ProversChaitanya Mangla, Sean B. Holden, Lawrence C. Paulson. cade 2022: 559-577 [doi]

On the Relation between Distributionally Robust Optimization and Data Curation (Student Abstract)Agnieszka Slowik, Léon Bottou, Sean B. Holden, Mateja Jamnik. AAAI 2022: 13053-13054 [doi]

Towards a Competitive 3-Player Mahjong AI using Deep Reinforcement LearningXiangyu Zhao, Sean B. Holden. cig 2022: 524-527 [doi]

2021

Machine Learning for Automated Theorem Proving: Learning to Solve SAT and QSATSean B. Holden. ftml, 14(6):807-989, 2021. [doi]

Structural Inductive Biases in Emergent CommunicationAgnieszka Slowik, Abhinav Gupta 0002, William L. Hamilton, Mateja Jamnik, Sean B. Holden, Chris Pal. cogsci 2021: [doi]

2020

Bayesian Optimisation of Solver Parameters in CBMCChaitanya Mangla, Sean B. Holden, Lawrence C. Paulson. smt 2021: 37-47 [doi]

Bayesian Optimisation for Premise Selection in Automated Theorem Proving (Student Abstract)Agnieszka Slowik, Chaitanya Mangla, Mateja Jamnik, Sean B. Holden, Lawrence C. Paulson. AAAI 2020: 13919-13920 [doi]

2019

Bayesian Optimisation for Heuristic Configuration in Automated Theorem ProvingAgnieszka Slowik, Chaitanya Mangla, Mateja Jamnik, Sean B. Holden, Lawrence C. Paulson. vampire 2019: 45-51 [doi]

2018

Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotationIrina M. Armean, Kathryn S. Lilley, Matthew W. B. Trotter, Nicholas Charles Victor Pilkington, Sean B. Holden. bioinformatics, 34(11):1884-1892, 2018. [doi]

2016

Learning from Heterogeneous Data Sources: An Application in Spatial ProteomicsLisa M. Breckels, Sean B. Holden, David Wojnar, Claire M. Mulvey, Andy Christoforou, Arnoud Groen, Matthew W. B. Trotter, Oliver Kohlbacher, Kathryn S. Lilley, Laurent Gatto. ploscb, 12(5), 2016. [doi]

2015

Learning Dynamic Systems from Time-series Data - An Application to Gene Regulatory NetworksIvo J. P. M. Timóteo, Sean B. Holden. icpram 2015: 324-332

2014

Machine Learning for First-Order Theorem Proving - Learning to Select a Good HeuristicJames P. Bridge, Sean B. Holden, Lawrence C. Paulson. JAR, 53(2):141-172, 2014. [doi]

2007

The Generalized FITC ApproximationAndrew Naish-Guzman, Sean B. Holden. nips 2008: 1057-1064 [doi]

Robust Regression with Twinned Gaussian ProcessesAndrew Naish-Guzman, Sean B. Holden. nips 2008: 1065-1072 [doi]

2005

On the Explicit Use of Example Weights in the Construction of ClassifiersAndrew Naish-Guzman, Sean B. Holden, Ulrich Paquet. icann 2005: 307-312 [doi]

Bayesian Hierarchical Ordinal RegressionUlrich Paquet, Sean B. Holden, Andrew Naish-Guzman. icann 2005: 267-272 [doi]

2002

Drug Design by Machine Learning: Support Vector Machines for Pharmaceutical Data AnalysisRobert Burbidge, Matthew W. B. Trotter, Bernard F. Buxton, Sean B. Holden. CANDC, 26(1):5-14, 2002. [doi]

2001

STAR - Sparsity through Automated RejectionRobert Burbidge, Matthew W. B. Trotter, Bernard F. Buxton, Sean B. Holden. iwann 2001: 653-660 [doi]

Performance Degradation in BoostingJeevani Wickramaratna, Sean B. Holden, Bernard F. Buxton. mcs 2001: 11-21 [doi]

1998

Cross-Validation for Binary Classification by Real-Valued Functions: Theoretical AnalysisMartin Anthony, Sean B. Holden. colt 1998: 218-229 [doi]

1997

Average-Case Learning Curves for Radial Basis Function NetworksSean B. Holden, Mahesan Niranjan. neco, 9(2):441-460, 1997. [doi]

1996

PAC-Like Upper Bounds for the Sample Complexity of Leave-one-Out Cross-ValidationSean B. Holden. colt 1996: 41-50 [doi]

1995

Generalization and PAC learning: some new results for the class of generalized single-layer networksSean B. Holden, Peter J. W. Rayner. tnn, 6(2):368-380, 1995. [doi]

On the statistical physics of radial basis function networksSean B. Holden, Mahesan Niranjan. npl, 2(4):16-19, 1995. [doi]

On the practical applicability of VC dimension boundsSean B. Holden, Mahesan Niranjan. neco, 7(6):1265-1288, 1995. [doi]

1994

Quantifying Generalization in Linearly Weighted Neural NetworksMartin Anthony, Sean B. Holden. compsys, 8(2), 1994. [doi]

1993

On the Power of Polynomial Discriminators and Radial Basis Function NetworksMartin Anthony, Sean B. Holden. colt 1993: 158-164 [doi]

On the theory of generalization and self-structuring in linearly weighted connectionist networksSean B. Holden. PhD thesis, University of Cambridge, UK, 1993. [doi]

1992

Generalization and learning in Volterra and radial basis function networks: a theoretical analysisSean B. Holden, Peter J. W. Rayner. icassp 1992: 273-276 [doi]

1991

Removal of degeneracy in adaptive Volterra networks by dynamic structuringM. R. Lynch, Peter J. W. Rayner, Sean B. Holden. icassp 1991: 2069-2072 [doi]

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