John Shawe-Taylor
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
79
Venues
33
Active years
1990–2026
Best venue rank
A*
Where they publish
- A*ICML15 papers
- A*COLT11 papers
- A*AAAI7 papers
- BALT6 papers
- BICONIP5 papers
- BIJCNN3 papers
- BESANN3 papers
- BEDM2 papers
- AAISTATS2 papers
- CEAMT2 papers
- NationalCOMSNETS1 paper
- A*IJCAI1 paper
- BCHIIR1 paper
- AIUI1 paper
- AWSDM1 paper
- A*EMNLP1 paper
- ACIKM1 paper
- CICPRAM1 paper
- CBIBE1 paper
- AUAI1 paper
- BETRA1 paper
- BFG1 paper
- ANAACL1 paper
- CADMA1 paper
- BSSPR1 paper
- CPACLIC1 paper
- ADIS1 paper
- CIDEAL1 paper
- CPATAT1 paper
- BFPL1 paper
- BDCC1 paper
- AGD1 paper
- BCPM1 paper
Papers
79 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2026 | COMSNETS | Green AI Orchestration Bridging Trustworthy AI and Edge AI through tinyML for Frugal Intelligence. | Joao Pita Costa, Ioana Ntinou, Marco Zennaro, John Shawe-Taylor |
| 2025 | AAAI | General Uncertainty Estimation with Delta Variances. | Simon Schmitt, John Shawe-Taylor, Hado van Hasselt |
| 2025 | IJCAI | Human-AI Coevolution (Abstract Reprint). | Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-Lszl Barabsi, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, Jnos Kertsz, Alistair Knott, Yannis E. Ioannidis, Paul Lukowicz, Andrea Passarella, Alex 'Sandy' Pentland, John Shawe-Taylor, Alessandro Vespignani |
| 2024 | AAAI | A Toolbox for Modelling Engagement with Educational Videos. | Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman Srazali, Mara Prez-Ortiz, Emine Yilmaz, John Shawe-Taylor, Sahan Bulathwela |
| 2023 | AAAI | Exploration via Epistemic Value Estimation. | Simon Schmitt, John Shawe-Taylor, Hado van Hasselt |
| 2022 | AAAI | Chaining Value Functions for Off-Policy Learning. | Simon Schmitt, John Shawe-Taylor, Hado van Hasselt |
| 2022 | CHIIR | Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos? | Mara Prez-Ortiz, Sahan Bulathwela, Claire Dormann, Meghana Verma, Stefan Kreitmayer, Richard Noss, John Shawe-Taylor, Yvonne Rogers, Emine Yilmaz |
| 2022 | EDM | Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments. | Sahan Bulathwela, Meghana Verma, Mara Prez-Ortiz, Emine Yilmaz, John Shawe-Taylor |
| 2022 | ICONIP | Correlation Based Semantic Transfer with Application to Domain Adaptation. | Florina-Cristina Calnegru, John Shawe-Taylor, Iasonas Kokkinos, Razvan Pascanu |
| 2021 | IUI | X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI. | Mara Prez-Ortiz, Claire Dormann, Yvonne Rogers, Sahan Bulathwela, Stefan Kreitmayer, Emine Yilmaz, Richard Noss, John Shawe-Taylor |
| 2020 | AAAI | Towards an Integrative Educational Recommender for Lifelong Learners (Student Abstract). | Sahan Bulathwela, Mara Prez-Ortiz, Emine Yilmaz, John Shawe-Taylor |
| 2020 | AAAI | TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources. | Sahan Bulathwela, Mara Prez-Ortiz, Emine Yilmaz, John Shawe-Taylor |
| 2020 | EDM | Predicting Engagement in Video Lectures. | Sahan Bulathwela, Mara Prez-Ortiz, Aldo Lipani, Emine Yilmaz, John Shawe-Taylor |
| 2020 | IJCNN | Adaptive Mechanism Design: Learning to Promote Cooperation. | Tobias Baumann, Thore Graepel, John Shawe-Taylor |
| 2020 | IJCNN | Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning. | Jun Yamada, John Shawe-Taylor, Zafeirios Fountas |
| 2020 | WSDM | SUM'20: State-based User Modelling. | Sahan Bulathwela, Mara Prez-Ortiz, Rishabh Mehrotra, Davor Orlic, Colin de la Higuera, John Shawe-Taylor, Emine Yilmaz |
| 2018 | EMNLP | Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding. | Gaurav Singh, James Thomas, Iain James Marshall, John Shawe-Taylor, Byron C. Wallace |
| 2017 | AISTATS | Localized Lasso for High-Dimensional Regression. | Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski |
| 2017 | CIKM | A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation. | Gaurav Singh, Iain James Marshall, James Thomas, John Shawe-Taylor, Byron C. Wallace |
| 2016 | AAAI | Compressed Conditional Mean Embeddings for Model-Based Reinforcement Learning. | Guy Lever, John Shawe-Taylor, Ronnie Stafford, Csaba Szepesvri |
| 2016 | IJCNN | Distributed variance regularized Multitask Learning. | Michele Donini, David Martnez-Rego, Martin Goodson, John Shawe-Taylor, Massimiliano Pontil |
| 2014 | ICONIP | Retrieval of Experiments by Efficient Comparison of Marginal Likelihoods. | Sohan Seth, John Shawe-Taylor, Samuel Kaski |
| 2014 | ICPRAM | Deep-er Kernels. | John Shawe-Taylor |
| 2013 | BIBE | Drug screening with Elastic-net multiple kernel learning. | Kitsuchart Pasupa, Zakria Hussain, John Shawe-Taylor, Peter Willett |
| 2013 | ICML | Smooth Operators. | Steffen Grnewlder, Arthur Gretton, John Shawe-Taylor |
| 2013 | ICONIP | Challenges in Representation Learning: A Report on Three Machine Learning Contests. | Ian J. Goodfellow, Dumitru Erhan, Pierre Luc Carrier, Aaron C. Courville, Mehdi Mirza, Benjamin Hamner, William Cukierski, Yichuan Tang, David Thaler, Dong-Hyun Lee, Yingbo Zhou, Chetan Ramaiah, Fangxiang Feng, Ruifan Li, Xiaojie Wang, Dimitris Athanasakis, John Shawe-Taylor, Maxim Milakov, John Park, Radu Tudor Ionescu, Marius Popescu, Cristian Grozea, James Bergstra, Jingjing Xie, Lukasz Romaszko, Bing Xu, Chuang Zhang, Yoshua Bengio |
| 2012 | UAI | PAC-Bayesian Inequalities for Martingales. | Yevgeny Seldin, Franois Laviolette, Nicol Cesa-Bianchi, John Shawe-Taylor, Peter Auer |
| 2010 | ALT | A PAC-Bayes Bound for Tailored Density Estimation. | Matthew Higgs, John Shawe-Taylor |
| 2010 | ALT | Distribution-Dependent PAC-Bayes Priors. | Guy Lever, Franois Laviolette, John Shawe-Taylor |
| 2010 | ETRA | Learning relevant eye movement feature spaces across users. | Zakria Hussain, Kitsuchart Pasupa, John Shawe-Taylor |
| 2009 | EAMT | Sentence-level confidence estimation for MT. | Lucia Specia, Nicola Cancedda, Marc Dymetman, Craig Saunders, Marco Turchi, Nello Cristianini, Zhuoran Wang, John Shawe-Taylor |
| 2009 | EAMT | Large-margin structural prediction via linear programming. | Zhuoran Wang, John Shawe-Taylor, Sndor Szedmk |
| 2009 | FG | Prior Knowledge in Learning Finite Parameter Spaces. | Dorota Glowacka, Louis Dorard, Alan Medlar, John Shawe-Taylor |
| 2007 | ICML | Approximate maximum margin algorithms with rules controlled by the number of mistakes. | Petroula Tsampouka, John Shawe-Taylor |
| 2007 | ICONIP | Using Image Stimuli to Drive fMRI Analysis. | David R. Hardoon, Janaina Mouro Miranda, Michael J. Brammer, John Shawe-Taylor |
| 2007 | ICONIP | Using Generalization Error Bounds to Train the Set Covering Machine. | Zakria Hussain, John Shawe-Taylor |
| 2007 | NAACL | Kernel Regression Based Machine Translation. | Zhuoran Wang, John Shawe-Taylor, Sndor Szedmk |
| 2006 | ADMA | A Correlation Approach for Automatic Image Annotation. | David R. Hardoon, Craig Saunders, Sndor Szedmk, John Shawe-Taylor |
| 2006 | ESANN | Synthesis of maximum margin and multiview learning using unlabeled data. | Sndor Szedmk, John Shawe-Taylor |
| 2006 | ICML | A probabilistic model for text kernels. | Alain D. Lehmann, John Shawe-Taylor |
| 2005 | ALT | Mixture of Vector Experts. | Matthew Henderson, John Shawe-Taylor, Janez Zerovnik |
| 2005 | ICML | Learning hierarchical multi-category text classification models. | Juho Rousu, Craig Saunders, Sndor Szedmk, John Shawe-Taylor |
| 2004 | ALT | Complexity of Pattern Classes and Lipschitz Property. | Amiran Ambroladze, John Shawe-Taylor |
| 2004 | SSPR | Texture Classification by Combining Wavelet and Contourlet Features. | Shutao Li, John Shawe-Taylor |
| 2003 | AISTATS | Refining Kernels for Regression and Uneven Classification Problems. | Jaz S. Kandola, John Shawe-Taylor |
| 2003 | COLT | When Is Small Beautiful? | Amiran Ambroladze, John Shawe-Taylor |
| 2003 | COLT | Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming. | Jaz S. Kandola, Thore Graepel, John Shawe-Taylor |
| 2003 | ICML | Linear Programming Boosting for Uneven Datasets. | Jure Leskovec, John Shawe-Taylor |
| 2003 | ICML | The Set Covering Machine with Data-Dependent Half-Spaces. | Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova |
| 2003 | PACLIC | The SVM With Uneven Margins and Chinese Document Categorization. | Yaoyong Li, John Shawe-Taylor |
| 2002 | ALT | 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 |
| 2002 | DIS | 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 |
| 2002 | ICML | The Perceptron Algorithm with Uneven Margins. | Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz S. Kandola |
| 2002 | ICML | Syllables and other String Kernel Extensions. | Craig Saunders, Hauke Tschach, John Shawe-Taylor |
| 2001 | ICML | Latent Semantic Kernels. | Nello Cristianini, John Shawe-Taylor, Huma Lodhi |
| 2001 | ICML | Composite Kernels for Hypertext Categorisation. | Thorsten Joachims, Nello Cristianini, John Shawe-Taylor |
| 2001 | ICML | Learning with the Set Covering Machine. | Mario Marchand, John Shawe-Taylor |
| 2000 | COLT | Generalisation Error Bounds for Sparse Linear Classifiers. | Thore Graepel, Ralf Herbrich, John Shawe-Taylor |
| 2000 | COLT | Sparsity vs. Large Margins for Linear Classifiers. | Ralf Herbrich, Thore Graepel, John Shawe-Taylor |
| 2000 | ICML | A Column Generation Algorithm For Boosting. | Kristin P. Bennett, Ayhan Demiriz, John Shawe-Taylor |
| 2000 | ICML | Direct Bayes Point Machines. | Matthias Rychetsky, John Shawe-Taylor, Manfred Glesner |
| 2000 | IDEAL | Boosting the Margin Distribution. | Huma Lodhi, Grigoris I. Karakoulas, John Shawe-Taylor |
| 2000 | PATAT | Graph Colouring by Maximal Evidence Edge Adding. | Barry Rising, John Shawe-Taylor, Janez Zerovnik |
| 1999 | COLT | Covering Numbers for Support Vector Machines. | Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson |
| 1999 | COLT | Further Results on the Margin Distribution. | John Shawe-Taylor, Nello Cristianini |
| 1999 | ESANN | A multiplicative updating algorithm for training support vector machine. | Nello Cristianini, Colin Campbell, John Shawe-Taylor |
| 1999 | ICML | Large Margin Trees for Induction and Transduction. | Donghui Wu, Kristin P. Bennett, Nello Cristianini, John Shawe-Taylor |
| 1998 | ICML | Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space. | Nello Cristianini, John Shawe-Taylor, Peter Sykacek |
| 1997 | COLT | A PAC Analysis of a Bayesian Estimator. | John Shawe-Taylor, Robert C. Williamson |
| 1997 | FPL | Parallel Graph colouring using FPGAs. | Barry Rising, Max van Daalen, Peter Burge, John Shawe-Taylor |
| 1996 | COLT | A Framework for Structural Risk Minimisation. | John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony |
| 1996 | DCC | Learning to Compress Ergodic Sources. | Jonathan Baxter, John Shawe-Taylor |
| 1995 | ALT | The Complexity of Learning Minor Closed Graph Classes. | Carlos Domingo, John Shawe-Taylor |
| 1995 | COLT | Sample Sizes for Sigmoidal Neural Networks. | John Shawe-Taylor |
| 1995 | ESANN | Neural networks for invariant pattern recognition. | Jeffrey Wood, John Shawe-Taylor |
| 1994 | GD | Molecular Graph Eigenvectors for Molecular Coordinates. | Patrick W. Fowler, Tomaz Pisanski, John Shawe-Taylor |
| 1992 | COLT | On Exact Specification by Examples. | Martin Anthony, Graham R. Brightwell, David A. Cohen, John Shawe-Taylor |
| 1992 | CPM | Fast Multiple Keyword Searching. | Jong Yong Kim, John Shawe-Taylor |
| 1990 | COLT | The Learnability of Formal Concepts. | Martin Anthony, Norman Biggs, John Shawe-Taylor |