Shawe-Taylor, John

92 publications

AAAI 2025 General Uncertainty Estimation with Delta Variances Simon Schmitt, John Shawe-Taylor, Hado van Hasselt

IJCAI 2025 Human-AI Coevolution (Abstract Reprint) Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-László Barabási, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, János Kertész, Alistair Knott, Yannis E. Ioannidis, Paul Lukowicz, Andrea Passarella, Alex 'Sandy' Pentland, John Shawe-Taylor, Alessandro Vespignani

AAAI 2024 A Toolbox for Modelling Engagement with Educational Videos Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman Srazali, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor, Sahan Bulathwela

NeurIPS 2024 Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound Reuben Adams, John Shawe-Taylor, Benjamin Guedj

AAAI 2023 Exploration via Epistemic Value Estimation Simon Schmitt, John Shawe-Taylor, Hado van Hasselt

AAAI 2022 Chaining Value Functions for Off-Policy Learning Simon Schmitt, John Shawe-Taylor, Hado van Hasselt

JMLR 2021 Tighter Risk Certificates for Neural Networks María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári

NeurIPS 2020 PAC-Bayes Analysis Beyond the Usual Bounds Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvari, John Shawe-Taylor

AAAI 2020 Towards an Integrative Educational Recommender for Lifelong Learners (Student Abstract) Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor

AAAI 2020 TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor

MLJ 2017 High-Probability Minimax Probability Machines Simon Cousins, John Shawe-Taylor

AISTATS 2017 Localized Lasso for High-Dimensional Regression Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski

AAAI 2016 Compressed Conditional Mean Embeddings for Model-Based Reinforcement Learning Guy Lever, John Shawe-Taylor, Ronnie Stafford, Csaba Szepesvári

ICLR 2014 Learning Non-Linear Feature Maps, with an Application to Representation Learning Dimitris Athanasakis, John Shawe-Taylor, Delmiro Fernandez-Reyes

ICML 2013 Smooth Operators Steffen Grunewalder, Gretton Arthur, John Shawe-Taylor

AISTATS 2012 Data Dependent Kernels in Nearly-Linear Time Guy Lever, Tom Diethe, John Shawe-Taylor

JMLR 2012 PAC-Bayes Bounds with Data Dependent Priors Emilio Parrado-Hernández, Amiran Ambroladze, John Shawe-Taylor, Shiliang Sun

UAI 2012 PAC-Bayesian Inequalities for Martingales Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor, Peter Auer

JMLR 2011 Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning Dorota Głowacka, John Shawe-Taylor, Alex Clark, Colin de la Higuera, Mark Johnson

MLJ 2011 Sparse Canonical Correlation Analysis David R. Hardoon, John Shawe-Taylor

ALT 2010 A PAC-Bayes Bound for Tailored Density Estimation Matthew Higgs, John Shawe-Taylor

ECML-PKDD 2010 Constructing Nonlinear Discriminants from Multiple Data Views Tom Diethe, David R. Hardoon, John Shawe-Taylor

ACML 2010 Content-Based Image Retrieval with Multinomial Relevance Feedback Dorota Glowacka, John Shawe-Taylor

MLJ 2010 Decomposing the Tensor Kernel Support Vector Machine for Neuroscience Data with Structured Labels David R. Hardoon, John Shawe-Taylor

ALT 2010 Distribution-Dependent PAC-Bayes Priors Guy Lever, François Laviolette, John Shawe-Taylor

ECML-PKDD 2010 Exploration-Exploitation of Eye Movement Enriched Multiple Feature Spaces for Content-Based Image Retrieval Zakria Hussain, Alex Po Leung, Kitsuchart Pasupa, David R. Hardoon, Peter Auer, John Shawe-Taylor

JMLR 2010 Sparse Semi-Supervised Learning Using Conjugate Functions Shiliang Sun, John Shawe-Taylor

MLJ 2009 Convergence Analysis of Kernel Canonical Correlation Analysis: Theory and Practice David R. Hardoon, John Shawe-Taylor

AISTATS 2009 Large-Margin Structured Prediction via Linear Programming Zhuoran Wang, John Shawe-Taylor

AISTATS 2009 Matching Pursuit Kernel Fisher Discriminant Analysis Tom Diethe, Zakria Hussain, David Hardoon, John Shawe-Taylor

AISTATS 2009 PAC-Bayes Analysis of Maximum Entropy Classification John Shawe-Taylor, David Hardoon

AISTATS 2007 A Framework for Probability Density Estimation John Shawe-Taylor, Alex Dolia

ICML 2007 Approximate Maximum Margin Algorithms with Rules Controlled by the Number of Mistakes Petroula Tsampouka, John Shawe-Taylor

AISTATS 2007 Information Retrieval by Inferring Implicit Queries from Eye Movements David R. Hardoon, John Shawe-Taylor, Antti Ajanki, Kai Puolamäki, Samuel Kaski

JMLR 2007 Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data Zakria Hussain, François Laviolette, Mario Marchand, John Shawe-Taylor, Spencer Charles Brubaker, Matthew D. Mullin

ICML 2006 A Probabilistic Model for Text Kernels Alain D. Lehmann, John Shawe-Taylor

ECML-PKDD 2006 Constant Rate Approximate Maximum Margin Algorithms Petroula Tsampouka, John Shawe-Taylor

JMLR 2006 Kernel-Based Learning of Hierarchical Multilabel Classification Models Juho Rousu, Craig Saunders, Sandor Szedmak, John Shawe-Taylor

ECML-PKDD 2006 The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces Alexander N. Dolia, Tijl De Bie, Christopher J. Harris, John Shawe-Taylor, D. M. Titterington

ECML-PKDD 2005 Analysis of Generic Perceptron-like Large Margin Classifiers Petroula Tsampouka, John Shawe-Taylor

JMLR 2005 Efficient Computation of Gapped Substring Kernels on Large Alphabets Juho Rousu, John Shawe-Taylor

ICML 2005 Learning Hierarchical Multi-Category Text Classification Models Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor

ALT 2005 Mixture of Vector Experts Matthew Henderson, John Shawe-Taylor, Janez Zerovnik

MLJ 2005 PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification Thore Graepel, Ralf Herbrich, John Shawe-Taylor

NeCo 2004 Canonical Correlation Analysis: An Overview with Application to Learning Methods David R. Hardoon, Sándor Szedmák, John Shawe-Taylor

ALT 2004 Complexity of Pattern Classes and Lipschitz Property Amiran Ambroladze, John Shawe-Taylor

ECML-PKDD 2004 Using String Kernels to Identify Famous Performers from Their Playing Style Craig Saunders, David R. Hardoon, John Shawe-Taylor, Gerhard Widmer

ICML 2003 Linear Programming Boosting for Uneven Datasets Jure Leskovec, John Shawe-Taylor

COLT 2003 Reducing Kernel Matrix Diagonal Dominance Using Semi-Definite Programming Jaz S. Kandola, Thore Graepel, John Shawe-Taylor

AISTATS 2003 Refining Kernels for Regression and Uneven Classification Problems Jaz S. Kandola, John Shawe-Taylor

ICML 2003 The Set Covering Machine with Data-Dependent Half-Spaces Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova

COLT 2003 When Is Small Beautiful? Amiran Ambroladze, John Shawe-Taylor

NeurIPS 2002 Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis Alexei Vinokourov, Nello Cristianini, John Shawe-Taylor

MLJ 2002 Linear Programming Boosting via Column Generation Ayhan Demiriz, Kristin P. Bennett, John Shawe-Taylor

ALT 2002 On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz S. Kandola

NeurIPS 2002 PAC-Bayes & Margins John Langford, John Shawe-Taylor

ICML 2002 Syllables and Other String Kernel Extensions Craig Saunders, Hauke Tschach, John Shawe-Taylor

JMLR 2002 Text Classification Using String Kernels Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Chris Watkins

ICML 2002 The Perceptron Algorithm with Uneven Margins Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz S. Kandola

JMLR 2002 The Set Covering Machine Mario Marchand, John Shawe-Taylor

ICML 2001 Composite Kernels for Hypertext Categorisation Thorsten Joachims, Nello Cristianini, John Shawe-Taylor

NeCo 2001 Estimating the Support of a High-Dimensional Distribution Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alexander J. Smola, Robert C. Williamson

ICML 2001 Latent Semantic Kernels Nello Cristianini, John Shawe-Taylor, Huma Lodhi

ICML 2001 Learning with the Set Covering Machine Mario Marchand, John Shawe-Taylor

NeurIPS 2001 On Kernel-Target Alignment Nello Cristianini, John Shawe-Taylor, André Elisseeff, Jaz S. Kandola

NeurIPS 2001 On the Concentration of Spectral Properties John Shawe-Taylor, Nello Cristianini, Jaz S. Kandola

NeurIPS 2001 Spectral Kernel Methods for Clustering Nello Cristianini, John Shawe-Taylor, Jaz S. Kandola

ICML 2000 A Column Generation Algorithm for Boosting Kristin P. Bennett, Ayhan Demiriz, John Shawe-Taylor

ICML 2000 Direct Bayes Point Machines Matthias Rychetsky, John Shawe-Taylor, Manfred Glesner

MLJ 2000 Enlarging the Margins in Perceptron Decision Trees Kristin P. Bennett, Nello Cristianini, John Shawe-Taylor, Donghui Wu

COLT 2000 Generalisation Error Bounds for Sparse Linear Classifiers Thore Graepel, Ralf Herbrich, John Shawe-Taylor

COLT 2000 Sparsity vs. Large Margins for Linear Classifiers Ralf Herbrich, Thore Graepel, John Shawe-Taylor

NeurIPS 2000 Text Classification Using String Kernels Huma Lodhi, John Shawe-Taylor, Nello Cristianini, Christopher J. C. H. Watkins

COLT 1999 Covering Numbers for Support Vector Machines Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson

COLT 1999 Further Results on the Margin Distribution John Shawe-Taylor, Nello Cristianini

MLJ 1999 Introducing the Special Issue of Machine Learning Selected from Papers Presented at the 1997 Conference on Computational Learning Theory, COLT'97 John Shawe-Taylor

NeurIPS 1999 Large Margin DAGs for Multiclass Classification John C. Platt, Nello Cristianini, John Shawe-Taylor

ICML 1999 Large Margin Trees for Induction and Transduction Donghui Wu, Kristin P. Bennett, Nello Cristianini, John Shawe-Taylor

NeurIPS 1999 Support Vector Method for Novelty Detection Bernhard Schölkopf, Robert C. Williamson, Alex J. Smola, John Shawe-Taylor, John C. Platt

NeurIPS 1999 The Entropy Regularization Information Criterion Alex J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson

ICML 1998 Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space Nello Cristianini, John Shawe-Taylor, Peter Sykacek

NeurIPS 1998 Dynamically Adapting Kernels in Support Vector Machines Nello Cristianini, Colin Campbell, John Shawe-Taylor

NeurIPS 1998 Optimizing Classifers for Imbalanced Training Sets Grigoris I. Karakoulas, John Shawe-Taylor

COLT 1997 A PAC Analysis of a Bayesian Estimator John Shawe-Taylor, Robert C. Williamson

NeurIPS 1997 Data-Dependent Structural Risk Minimization for Perceptron Decision Trees John Shawe-Taylor, Nello Cristianini

COLT 1996 A Framework for Structural Risk Minimisation John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony

NeurIPS 1995 Generalisation of a Class of Continuous Neural Networks John Shawe-Taylor, Jieyu Zhao

COLT 1995 Sample Sizes for Sigmoidal Neural Networks John Shawe-Taylor

ALT 1995 The Complexity of Learning Minor Closed Graph Classes Carlos Domingo, John Shawe-Taylor

COLT 1992 On Exact Specification by Examples Martin Anthony, Graham R. Brightwell, David A. Cohen, John Shawe-Taylor

NeurIPS 1991 Threshold Network Learning in the Presence of Equivalences John Shawe-Taylor

COLT 1990 The Learnability of Formal Concepts Martin Anthony, Norman Biggs, John Shawe-Taylor