John Shawe-Taylor

351 papers A* 39A 15B 22C 7Misc 1Journal 175Unranked 77

Year	Rank	Type	Title / Venue / Authors
2026	Misc	`conf`	Green AI Orchestration Bridging Trustworthy AI and Edge AI through tinyML for Frugal Intelligence. COMSNETS Joao Pita Costa, Ioana Ntinou, Marco Zennaro, John Shawe-Taylor
2026	J	`jnl`	Resilience-oriented decision making on the pre-shock intervention to road networks. Adv. Eng. Informatics Siyao Yang, Li Sun, Gang Li, John Shawe-Taylor, Haijiang Li, Bozidar Stojadinovic
2025	J	`jnl`	Elastic-Net Multiple Kernel Learning: Combining Multiple Data Sources for Prediction. CoRR Janaina Mourão Miranda, Zakria Hussain, Konstantinos Tsirlis, Christophe Phillips, John Shawe-Taylor
2025	A*	`conf`	General Uncertainty Estimation with Delta Variances. AAAI Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
2025	J	`jnl`	General Uncertainty Estimation with Delta Variances. CoRR Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
2025	A*	`conf`	Human-AI Coevolution (Abstract Reprint). IJCAI 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
2025	J	`jnl`	Human-AI coevolution. Artif. Intell. 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
2025	J	`jnl`	Some theoretical improvements on the tightness of PAC-Bayes risk certificates for neural networks. CoRR Diego García-Pérez, Emilio Parrado-Hernández, John Shawe-Taylor
2025	J	`jnl`	TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models. CoRR Sahan Bulathwela, Daniel van Niekerk, Jarrod Shipton, María Pérez-Ortiz, Benjamin Rosman, John Shawe-Taylor
2024	A*	`conf`	A Toolbox for Modelling Engagement with Educational Videos. AAAI Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman Srazali, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor, Sahan Bulathwela
2024	J	`jnl`	A Toolbox for Modelling Engagement with Educational Videos. CoRR Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor, Sahan Bulathwela
2024	J	`jnl`	Aligning Generalisation Between Humans and Machines. CoRR Filip Ilievski, Barbara Hammer, Frank van Harmelen, Benjamin Paassen, Sascha Saralajew, Ute Schmid, Michael Biehl, Marianna Bolognesi, Xin Luna Dong, Kiril Gashteovski, Pascal Hitzler, Giuseppe Marra, Pasquale Minervini, Martin Mundt, Axel-Cyrille Ngonga Ngomo, Alessandro Oltramari, Gabriella Pasi, Zeynep G. Saribatur, Luciano Serafini, John Shawe-Taylor, Vered Shwartz, Gabriella Skitalinskaya, Clemens Stachl, Gido M. van de Ven, Thomas Villmann
2024	J	`jnl`	Contrastive learning of T cell receptor representations. CoRR Yuta Nagano, Andrew G. T. Pyo, Martina Milighetti, James Henderson, John Shawe-Taylor, Benny Chain, Andreas Tiffeau-Mayer
2024	A*	`conf`	Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound. NeurIPS Reuben Adams, John Shawe-Taylor, Benjamin Guedj
2024	J	`jnl`	Transfer and zero-shot learning for scalable weed detection and classification in UAV images. Knowl. Based Syst. Nicolas Belissent, José M. Peña, Gustavo A. Mesías-Ruiz, John Shawe-Taylor, María Pérez-Ortiz
2023	J	`jnl`	Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models. CoRR Theodore Wolf, Nantas Nardelli, John Shawe-Taylor, María Pérez-Ortiz
2023	A*	`conf`	Exploration via Epistemic Value Estimation. AAAI Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
2023	J	`jnl`	Exploration via Epistemic Value Estimation. CoRR Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
2023	J	`jnl`	From fear to action: AI governance and opportunities for all. Frontiers Comput. Sci. Kevin Baum, Joanna Bryson, Frank Dignum, Virginia Dignum, Marko Grobelnik, Holger H. Hoos, Morten Irgens, Paul Lukowicz, Catelijne Muller, Francesca Rossi, John Shawe-Taylor, Andreas Theodorou, Ricardo Vinuesa
2023	—	`ed.`	HHAI 2023: Augmenting Human Intellect - Proceedings of the Second International Conference on Hybrid Human-Artificial Intelligence, June 26-30, 2023, Munich, Germany. HHAI Paul Lukowicz, Sven Mayer, Janin Koch, John Shawe-Taylor, Ilaria Tiddi
2023	J	`jnl`	Model validation using mutated training labels: An exploratory study. Neurocomputing Jie M. Zhang, Mark Harman, Benjamin Guedj, Earl T. Barr, John Shawe-Taylor
2023	J	`jnl`	Seeking information about assistive technology: Exploring current practices, challenges, and the need for smarter systems. Int. J. Hum. Comput. Stud. Jamie Danemayer, Cathy Holloway, Youngjun Cho, Nadia Berthouze, Aneesha Singh, William Bhot, Ollie Dixon, Marko Grobelnik, John Shawe-Taylor
2023	J	`jnl`	Social AI and the Challenges of the Human-AI Ecosystem. CoRR Dino Pedreschi, Luca Pappalardo, 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
2022	B	`conf`	Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments. EDM Sahan Bulathwela, Meghana Verma, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
2022	J	`jnl`	Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments. CoRR Sahan Bulathwela, Meghana Verma, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
2022	A*	`conf`	Chaining Value Functions for Off-Policy Learning. AAAI Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
2022	J	`jnl`	Chaining Value Functions for Off-Policy Learning. CoRR Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
2022	J	`jnl`	Controlling Confusion via Generalisation Bounds. CoRR Reuben Adams, John Shawe-Taylor, Benjamin Guedj
2022	—	`conf`	Correlation Based Semantic Transfer with Application to Domain Adaptation. ICONIP (1) Florina-Cristina Calnegru, John Shawe-Taylor, Iasonas Kokkinos, Razvan Pascanu
2022	J	`jnl`	Human-Centered Artificial Intelligence (Dagstuhl Seminar 22262). Dagstuhl Reports Wendy E. Mackay, John Shawe-Taylor, Frank van Harmelen
2022	J	`jnl`	Stability-based PAC-Bayes analysis for multi-view learning algorithms. Inf. Fusion Shiliang Sun, Mengran Yu, John Shawe-Taylor, Liang Mao
2022	J	`jnl`	TransductGAN: a Transductive Adversarial Model for Novelty Detection. CoRR Najiba Toron, Janaina Mourão Miranda, John Shawe-Taylor
2022	B	`conf`	Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos? CHIIR María Pérez-Ortiz, Sahan Bulathwela, Claire Dormann, Meghana Verma, Stefan Kreitmayer, Richard Noss, John Shawe-Taylor, Yvonne Rogers, Emine Yilmaz
2022	J	`jnl`	Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos? CoRR María Pérez-Ortiz, Sahan Bulathwela, Claire Dormann, Meghana Verma, Stefan Kreitmayer, Richard Noss, John Shawe-Taylor, Yvonne Rogers, Emine Yilmaz
2021	J	`jnl`	An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All. CoRR María Pérez-Ortiz, Erik Novak, Sahan Bulathwela, John Shawe-Taylor
2021	J	`jnl`	Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution. CoRR Sahan Bulathwela, María Pérez-Ortiz, Catherine Holloway, John Shawe-Taylor
2021	J	`jnl`	Learning PAC-Bayes Priors for Probabilistic Neural Networks. CoRR María Pérez-Ortiz, Omar Rivasplata, Benjamin Guedj, Matthew Gleeson, Jingyu Zhang, John Shawe-Taylor, Miroslaw Bober, Josef Kittler
2021	J	`jnl`	PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses. Entropy Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor
2021	J	`jnl`	PEEK: A Large Dataset of Learner Engagement with Educational Videos. CoRR Sahan Bulathwela, María Pérez-Ortiz, Erik Novak, Emine Yilmaz, John Shawe-Taylor
2021	J	`jnl`	Progress in Self-Certified Neural Networks. CoRR María Pérez-Ortiz, Omar Rivasplata, Emilio Parrado-Hernández, Benjamin Guedj, John Shawe-Taylor
2021	J	`jnl`	Road map for research on responsible artificial intelligence for development (AI4D) in African countries: The case study of agriculture. Patterns Arthur Gwagwa, Emre Kazim, Patti Kachidza, Airlie Hilliard, Kathleen Siminyu, Matthew Smith, John Shawe-Taylor
2021	J	`jnl`	Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems. CoRR Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
2021	J	`jnl`	Tighter Risk Certificates for Neural Networks. J. Mach. Learn. Res. María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári
2021	—	`conf`	X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI. IUI Companion María Pérez-Ortiz, Claire Dormann, Yvonne Rogers, Sahan Bulathwela, Stefan Kreitmayer, Emine Yilmaz, Richard Noss, John Shawe-Taylor
2020	J	`jnl`	A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings. CoRR Théophile Cantelobre, Benjamin Guedj, María Pérez-Ortiz, John Shawe-Taylor
2020	B	`conf`	Adaptive Mechanism Design: Learning to Promote Cooperation. IJCNN Tobias Baumann, Thore Graepel, John Shawe-Taylor
2020	J	`jnl`	Correlated Feature Selection with Extended Exclusive Group Lasso. CoRR Yuxin Sun, Benny Chain, Samuel Kaski, John Shawe-Taylor
2020	B	`conf`	Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning. IJCNN Jun Yamada, John Shawe-Taylor, Zafeirios Fountas
2020	J	`jnl`	Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning. CoRR Jun Yamada, John Shawe-Taylor, Zafeirios Fountas
2020	A*	`conf`	PAC-Bayes Analysis Beyond the Usual Bounds. NeurIPS Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe-Taylor
2020	J	`jnl`	PAC-Bayes Analysis Beyond the Usual Bounds. CoRR Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe-Taylor
2020	J	`jnl`	PAC-Bayes unleashed: generalisation bounds with unbounded losses. CoRR Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor
2020	B	`conf`	Predicting Engagement in Video Lectures. EDM Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz, John Shawe-Taylor
2020	J	`jnl`	Predicting Engagement in Video Lectures. CoRR Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz, John Shawe-Taylor
2020	J	`jnl`	Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy. Neurocomputing Luca Oneto, Michele Donini, Massimiliano Pontil, John Shawe-Taylor
2020	J	`jnl`	Report on the WSDM 2020 workshop on state-based user modelling (SUM'20). SIGIR Forum Sahan Bulathwela, María Pérez-Ortiz, Rishabh Mehrotra, Davor Orlic, Colin de la Higuera, John Shawe-Taylor, Emine Yilmaz
2020	A	`conf`	SUM'20: State-based User Modelling. WSDM Sahan Bulathwela, María Pérez-Ortiz, Rishabh Mehrotra, Davor Orlic, Colin de la Higuera, John Shawe-Taylor, Emine Yilmaz
2020	J	`jnl`	Tighter risk certificates for neural networks. CoRR María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári
2020	A*	`conf`	Towards an Integrative Educational Recommender for Lifelong Learners (Student Abstract). AAAI Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
2020	A*	`conf`	TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources. AAAI Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
2020	J	`jnl`	Upper and Lower Bounds on the Performance of Kernel PCA. CoRR Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor
2020	J	`jnl`	VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement. CoRR Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
2019	J	`jnl`	Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important. NeuroImage Michele Donini, João M. Monteiro, Massimiliano Pontil, Tim Hahn, Andreas J. Fallgatter, John Shawe-Taylor, Janaina Mourão Miranda
2019	J	`jnl`	Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation. CoRR Gaurav Singh, Zahra Sabet, John Shawe-Taylor, James Thomas
2019	J	`jnl`	Data-Driven Malaria Prevalence Prediction in Large Densely-Populated Urban Holoendemic sub-Saharan West Africa: Harnessing Machine Learning Approaches and 22-years of Prospectively Collected Data. CoRR Biobele J. Brown, Alexander A. Przybylski, Petru Manescu, Fabio Caccioli, Gbeminiyi Oyinloye, Muna Elmi, Michael J. Shaw, Vijay Pawar, Remy Claveau, John Shawe-Taylor, Mandayam A. Srinivasan, Nathaniel K. Afolabi, Adebola E. Orimadegun, Wasiu A. Ajetunmobi, Francis Akinkunmi, Olayinka Kowobari, Kikelomo Osinusi, Felix O. Akinbami, Samuel Omokhodion, Wuraola A. Shokunbi, Ikeoluwa Lagunju, Olugbemiro Sodeinde, Delmiro Fernandez-Reyes
2019	J	`jnl`	Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy. CoRR Petru Manescu, Lydia Neary-Zajiczek, Michael J. Shaw, Muna Elmi, Remy Claveau, Vijay Pawar, John Shawe-Taylor, Iasonas Kokkinos, Mandayam A. Srinivasan, Ikeoluwa Lagunju, Olugbemiro Sodeinde, Biobele J. Brown, Delmiro Fernandez-Reyes
2019	J	`jnl`	Perturbed Model Validation: A New Framework to Validate Model Relevance. CoRR Jie M. Zhang, Earl T. Barr, Benjamin Guedj, Mark Harman, John Shawe-Taylor
2019	J	`jnl`	Towards an Integrative Educational Recommender for Lifelong Learners. CoRR Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
2019	J	`jnl`	TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources. CoRR Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz, John Shawe-Taylor
2018	J	`jnl`	A Balanced Route Design for Min-Max Multiple-Depot Rural Postman Problem (MMMDRPP): a police patrolling case. Int. J. Geogr. Inf. Sci. Huanfa Chen, Tao Cheng, John Shawe-Taylor
2018	J	`jnl`	A Tutorial on Canonical Correlation Methods. ACM Comput. Surv. Viivi Uurtio, João M. Monteiro, Jaz S. Kandola, John Shawe-Taylor, Delmiro Fernandez-Reyes, Juho Rousu
2018	J	`jnl`	Adaptive Mechanism Design: Learning to Promote Cooperation. CoRR Tobias Baumann, Thore Graepel, John Shawe-Taylor
2018	A*	`conf`	Empirical Risk Minimization Under Fairness Constraints. NeurIPS Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil
2018	J	`jnl`	Empirical Risk Minimization under Fairness Constraints. CoRR Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil
2018	J	`jnl`	Faster Convergence & Generalization in DNNs. CoRR Gaurav Singh, John Shawe-Taylor
2018	J	`jnl`	Improving Active Learning in Systematic Reviews. CoRR Gaurav Singh, James Thomas, John Shawe-Taylor
2018	J	`jnl`	Interactional regions in cities: making sense of flows across networked systems. Int. J. Geogr. Inf. Sci. Kira Kempinska, Paul A. Longley, John Shawe-Taylor
2018	J	`jnl`	Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection. CoRR Luke R. Harries, Suyi Zhang, Geoffroy Dubourg-Felonneau, James H. R. Farmery, Jonathan Sinai, Belle Taylor, Nirmesh Patel, John W. Cassidy, John Shawe-Taylor, Harry W. Clifford
2018	A*	`conf`	PAC-Bayes bounds for stable algorithms with instance-dependent priors. NeurIPS Omar Rivasplata, Csaba Szepesvári, John Shawe-Taylor, Emilio Parrado-Hernández, Shiliang Sun
2018	J	`jnl`	PAC-Bayes bounds for stable algorithms with instance-dependent priors. CoRR Omar Rivasplata, Emilio Parrado-Hernández, John Shawe-Taylor, Shiliang Sun, Csaba Szepesvári
2018	—	`conf`	Sparse PLS hyper-parameters optimisation for investigating brain-behaviour relationships. PRNI Fabio S. Ferreira, Maria João Duarte Rosa, Michael Moutoussis, Ray Dolan, John Shawe-Taylor, John Ashburner, Janaina Mourão Miranda
2018	J	`jnl`	Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression. CoRR Seth Nabarro, Tristan Fletcher, John Shawe-Taylor
2018	A*	`conf`	Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding. EMNLP Gaurav Singh, James Thomas, Iain James Marshall, John Shawe-Taylor, Byron C. Wallace
2018	J	`jnl`	Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding. CoRR Gaurav Singh, James Thomas, Iain James Marshall, John Shawe-Taylor, Byron C. Wallace
2017	A	`conf`	A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation. CIKM Gaurav Singh, Iain James Marshall, James Thomas, John Shawe-Taylor, Byron C. Wallace
2017	J	`jnl`	A Tutorial on Canonical Correlation Methods. CoRR Viivi Uurtio, João M. Monteiro, Jaz S. Kandola, John Shawe-Taylor, Delmiro Fernandez-Reyes, Juho Rousu
2017	J	`jnl`	High-probability minimax probability machines. Mach. Learn. Simon Cousins, John Shawe-Taylor
2017	A	`conf`	Localized Lasso for High-Dimensional Regression. AISTATS Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski
2017	J	`jnl`	PAC-Bayes analysis of multi-view learning. Inf. Fusion Shiliang Sun, John Shawe-Taylor, Liang Mao
2016	—	`conf`	A multimodal multiple kernel learning approach to Alzheimer's disease detection. MLSP Michele Donini, João M. Monteiro, Massimiliano Pontil, John Shawe-Taylor, Janaina Mourão Miranda
2016	A*	`conf`	Compressed Conditional Mean Embeddings for Model-Based Reinforcement Learning. AAAI Guy Lever, John Shawe-Taylor, Ronnie Stafford, Csaba Szepesvári
2016	B	`conf`	Distributed variance regularized Multitask Learning. IJCNN Michele Donini, David Martínez-Rego, Martin Goodson, John Shawe-Taylor, Massimiliano Pontil
2016	J	`jnl`	Image Retrieval with a Bayesian Model of Relevance Feedback. CoRR Dorota Glowacka, Yee Whye Teh, John Shawe-Taylor
2016	J	`jnl`	Learning Shared Representations in Multi-task Reinforcement Learning. CoRR Diana Borsa, Thore Graepel, John Shawe-Taylor
2016	J	`jnl`	Neighborhood Sensitive Mapping for Zero-Shot Classification using Independently Learned Semantic Embeddings. CoRR Gaurav Singh, Fabrizio Silvestri, John Shawe-Taylor
2016	J	`jnl`	Sparse Network Lasso for Local High-dimensional Regression. CoRR Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski
2015	J	`jnl`	Challenges in representation learning: A report on three machine learning contests. Neural Networks 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
2015	—	`conf`	Multivariate Effect Ranking via Adaptive Sparse PLS. PRNI Joao M. Monteiro, Anil Rao, John Ashburner, John Shawe-Taylor, Janaina Mourão Miranda
2015	J	`jnl`	Sparse network-based models for patient classification using fMRI. NeuroImage Maria João Duarte Rosa, Liana Catarina Lima Portugal, Tim Hahn, Andreas J. Fallgatter, Marta I. Garrido, John Shawe-Taylor, Janaina Mourão Miranda
2014	J	`jnl`	Correction to "SCoRS - A Method Based on Stability for Feature Selection and Mapping in Neuroimaging". IEEE Trans. Medical Imaging Jane M. Rondina, Tim Hahn, Leticia de Oliveira, Andre F. Marquand, Thomas Dresler, Thomas Leitner, Andreas J. Fallgatter, John Shawe-Taylor, Janaina Mourão Miranda
2014	C	`conf`	Deep-er Kernels. ICPRAM John Shawe-Taylor
2014	J	`jnl`	Discovering brain regions relevant to obsessive-compulsive disorder identification through bagging and transduction. Medical Image Anal. Emilio Parrado-Hernández, Vanessa Gómez-Verdejo, Manel Martínez-Ramón, John Shawe-Taylor, Pino Alonso, Jesús Pujol, José Manuel Menchón, Narcís Cardoner, Carles Soriano-Mas
2014	—	`conf`	Learning Non-Linear Feature Maps, With An Application To Representation Learning. ICLR (Workshop Poster) Dimitris Athanasakis, John Shawe-Taylor, Delmiro Fernandez-Reyes
2014	—	`conf`	Leveraging Clinical Data to Enhance Localization of Brain Atrophy. MLINI@NIPS João M. Monteiro, Anil Rao, John Ashburner, John Shawe-Taylor, Janaina Mourão Miranda
2014	J	`jnl`	Manifold-preserving graph reduction for sparse semi-supervised learning. Neurocomputing Shiliang Sun, Zakria Hussain, John Shawe-Taylor
2014	—	`conf`	Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks. NIPS Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John Shawe-Taylor
2014	J	`jnl`	PAC-Bayes Analysis of Multi-view Learning. CoRR Shiliang Sun, John Shawe-Taylor
2014	J	`jnl`	PinView: Implicit Feedback in Content-Based Image Retrieval. CoRR Zakria Hussain, Arto Klami, Jussi Kujala, Alex Po Leung, Kitsuchart Pasupa, Peter Auer, Samuel Kaski, Jorma Laaksonen, John Shawe-Taylor
2014	—	`conf`	Retrieval of Experiments by Efficient Comparison of Marginal Likelihoods. ICONIP (2) Sohan Seth, John Shawe-Taylor, Samuel Kaski
2014	J	`jnl`	Retrieval of Experiments by Efficient Estimation of Marginal Likelihood. CoRR Sohan Seth, John Shawe-Taylor, Samuel Kaski
2014	J	`jnl`	SCoRS - A Method Based on Stability for Feature Selection and Apping in Neuroimaging. IEEE Trans. Medical Imaging Jane M. Rondina, Tim Hahn, Leticia de Oliveira, Andre F. Marquand, Thomas Dresler, Thomas Leitner, Andreas J. Fallgatter, John Shawe-Taylor, Janaina Mourão Miranda
2014	J	`jnl`	Tracking global changes induced in the CD4 T-cell receptor repertoire by immunization with a complex antigen using short stretches of CDR3 protein sequence. Bioinform. Niclas Thomas, Katharine Best, Mattia Cinelli, Shlomit Reich-Zeliger, Hilah Gal, Eric Shifrut, Asaf Madi, Nir Friedman, John Shawe-Taylor, Benny Chain
2013	J	`jnl`	A Comparison of Relaxations of Multiset Cannonical Correlation Analysis and Applications CoRR Jan Rupnik, Primoz Skraba, John Shawe-Taylor, Sabrina Guettes
2013	J	`jnl`	Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria. PLoS Comput. Biol. Juho Rousu, Daniel D. Agranoff, Olugbemiro Sodeinde, John Shawe-Taylor, Delmiro Fernandez-Reyes
2013	—	`conf`	Challenges in Representation Learning: A Report on Three Machine Learning Contests. ICONIP (3) 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
2013	J	`jnl`	Challenges in Representation Learning: A report on three machine learning contests. CoRR 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
2013	J	`jnl`	Decombinator: a tool for fast, efficient gene assignment in T-cell receptor sequences using a finite state machine. Bioinform. Niclas Thomas, James M. Heather, Wilfred Ndifon, John Shawe-Taylor, Benjamin Chain
2013	C	`conf`	Drug screening with Elastic-net multiple kernel learning. BIBE Kitsuchart Pasupa, Zakria Hussain, John Shawe-Taylor, Peter Willett
2013	J	`jnl`	Learning Non-Linear Feature Maps. CoRR Dimitris Athanasakis, John Shawe-Taylor, Delmiro Fernandez-Reyes
2013	—	`conf`	Smooth Operators. ICML (3) Steffen Grünewälder, Arthur Gretton, John Shawe-Taylor
2013	—	`conf`	Sparse Network-Based Models for Patient Classification Using fMRI. PRNI Maria João Duarte Rosa, Liana Catarina Lima Portugal, John Shawe-Taylor, Janaina Mourão Miranda
2013	—	`conf`	Stability-Based Multivariate Mapping Using SCoRS. PRNI Jane Maryam Rondina, John Shawe-Taylor, Janaina Mourão Miranda
2013	J	`jnl`	Tighter PAC-Bayes bounds through distribution-dependent priors. Theor. Comput. Sci. Guy Lever, François Laviolette, John Shawe-Taylor
2012	A	`conf`	Data dependent kernels in nearly-linear time. AISTATS Guy Lever, Tom Diethe, John Shawe-Taylor
2012	J	`jnl`	Forecasting foreign exchange rates using kernel methods. Expert Syst. Appl. Martin Sewell, John Shawe-Taylor
2012	J	`jnl`	MahNMF: Manhattan Non-negative Matrix Factorization CoRR Naiyang Guan, Dacheng Tao, Zhigang Luo, John Shawe-Taylor
2012	—	`conf`	PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits. ICML On-line Trading of Exploration and Exploitation Yevgeny Seldin, Nicolò Cesa-Bianchi, Peter Auer, François Laviolette, John Shawe-Taylor
2012	A	`conf`	PAC-Bayesian Inequalities for Martingales. UAI Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor, Peter Auer
2012	J	`jnl`	PAC-Bayesian Inequalities for Martingales. IEEE Trans. Inf. Theory Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor, Peter Auer
2012	J	`jnl`	PAC-bayes bounds with data dependent priors. J. Mach. Learn. Res. Emilio Parrado-Hernández, Amiran Ambroladze, John Shawe-Taylor, Shiliang Sun
2012	—	`conf`	Preface. ICML On-line Trading of Exploration and Exploitation Dorota Glowacka, Louis Dorard, John Shawe-Taylor
2012	—	`ed.`	Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2, Bellevue, Washington, USA, July 2, 2011 ICML On-line Trading of Exploration and Exploitation Dorota Glowacka, Louis Dorard, John Shawe-Taylor
2012	—	`conf`	Voxel Selection in MRI through Bagging and Conformal Analysis: Application to Detection of Obsessive Compulsive Disorder. PRNI Emilio Parrado-Hernández, Vanessa Gómez-Verdejo, Manel Martínez-Ramón, John Shawe-Taylor, Pino Alonso, Jesús Pujol, José Manuel Menchón, Narcís Cardoner, Carles Soriano-Mas
2011	—	`conf`	A New Feature Selection Method Based on Stability Theory - Exploring Parameters Space to Evaluate Classification Accuracy in Neuroimaging Data. MLINI Jane M. Rondina, John Shawe-Taylor, Janaina Mourão Miranda
2011	J	`jnl`	A Note on Improved Loss Bounds for Multiple Kernel Learning CoRR Zakria Hussain, John Shawe-Taylor
2011	J	`jnl`	A review of optimization methodologies in support vector machines. Neurocomputing John Shawe-Taylor, Shiliang Sun
2011	—	`ed.`	Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain. NIPS John Shawe-Taylor, Richard S. Zemel, Peter L. Bartlett, Fernando C. N. Pereira, Kilian Q. Weinberger
2011	J	`jnl`	Data-dependent kernels in nearly-linear time CoRR Guy Lever, Tom Diethe, John Shawe-Taylor
2011	J	`jnl`	Design and Generalization Analysis of Orthogonal Matching Pursuit Algorithms. IEEE Trans. Inf. Theory Zakria Hussain, John Shawe-Taylor, David R. Hardoon, Charanpal Dhanjal
2011	—	`conf`	Employing The Complete Face in AVSR to Recover from Facial Occlusions. WAPA Benjamin X. Hall, John Shawe-Taylor, Alan Johnston
2011	A	`conf`	Improved Loss Bounds For Multiple Kernel Learning. AISTATS Zakria Hussain, John Shawe-Taylor
2011	J	`jnl`	Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning. J. Mach. Learn. Res. Dorota Glowacka, John Shawe-Taylor, Alexander Clark, Colin de la Higuera, Mark Johnson
2011	J	`jnl`	Neural prediction of higher-order auditory sequence statistics. NeuroImage Nicholas Furl, Sukhbinder Kumar, Kai Alter, Simon Durrant, John Shawe-Taylor, Timothy D. Griffiths
2011	J	`jnl`	PAC-Bayes-Bernstein Inequality for Martingales and its Application to Multiarmed Bandits CoRR Yevgeny Seldin, Nicolò Cesa-Bianchi, Peter Auer, François Laviolette, John Shawe-Taylor
2011	—	`conf`	PAC-Bayesian Analysis of Contextual Bandits. NIPS Yevgeny Seldin, Peter Auer, François Laviolette, John Shawe-Taylor, Ronald Ortner
2011	J	`jnl`	PAC-Bayesian Analysis of Martingales and Multiarmed Bandits CoRR Yevgeny Seldin, François Laviolette, John Shawe-Taylor, Jan Peters, Peter Auer
2011	J	`jnl`	PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off CoRR Yevgeny Seldin, Nicolò Cesa-Bianchi, François Laviolette, Peter Auer, John Shawe-Taylor, Jan Peters
2011	J	`jnl`	PAC-Bayesian Inequalities for Martingales CoRR Yevgeny Seldin, François Laviolette, Nicolò Cesa-Bianchi, John Shawe-Taylor, Peter Auer
2011	J	`jnl`	Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine. NeuroImage Janaina Mourão Miranda, David R. Hardoon, Tim Hahn, Andre F. Marquand, Steven C. R. Williams, John Shawe-Taylor, Michael J. Brammer
2011	—	`conf`	Preface. WAPA Tom Diethe, José L. Balcázar, John Shawe-Taylor, Cristina Tîrnauca
2011	—	`ed.`	Proceedings of the Second Workshop on Applications of Pattern Analysis, WAPA 2011, Castro Urdiales, Spain, October 19-21, 2011 WAPA Tom Diethe, José L. Balcázar, John Shawe-Taylor, Cristina Tîrnauca
2011	J	`jnl`	Sparse canonical correlation analysis. Mach. Learn. David R. Hardoon, John Shawe-Taylor
2010	J	`jnl`	A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems. J. Signal Process. Syst. Yuan Shen, Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor, Remi Louis Barillec
2010	B	`conf`	A PAC-Bayes Bound for Tailored Density Estimation. ALT Matthew Higgs, John Shawe-Taylor
2010	J	`jnl`	A kernel regression framework for SMT. Mach. Transl. Zhuoran Wang, John Shawe-Taylor
2010	—	`ed.`	Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada. NIPS John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel, Aron Culotta
2010	—	`book`	An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Nello Cristianini, John Shawe-Taylor
2010	—	`conf`	Constructing Nonlinear Discriminants from Multiple Data Views. ECML/PKDD (1) Tom Diethe, David R. Hardoon, John Shawe-Taylor
2010	C	`conf`	Content-based Image Retrieval with Multinomial Relevance Feedback. ACML Dorota Glowacka, John Shawe-Taylor
2010	—	`conf`	Data Dependent Priors in PAC-Bayes Bounds. COMPSTAT John Shawe-Taylor, Emilio Parrado-Hernández, Amiran Ambroladze
2010	J	`jnl`	Decomposing the tensor kernel support vector machine for neuroscience data with structured labels. Mach. Learn. David R. Hardoon, John Shawe-Taylor
2010	B	`conf`	Distribution-Dependent PAC-Bayes Priors. ALT Guy Lever, François Laviolette, John Shawe-Taylor
2010	—	`conf`	Exploration-Exploitation of Eye Movement Enriched Multiple Feature Spaces for Content-Based Image Retrieval. ECML/PKDD (1) Zakria Hussain, Alex Po Leung, Kitsuchart Pasupa, David R. Hardoon, Peter Auer, John Shawe-Taylor
2010	J	`jnl`	Gaussian Process Bandits for Tree Search CoRR Louis Dorard, John Shawe-Taylor
2010	B	`conf`	Learning relevant eye movement feature spaces across users. ETRA Zakria Hussain, Kitsuchart Pasupa, John Shawe-Taylor
2010	—	`conf`	Multiple Kernel Learning on the Limit Order Book. WAPA Tristan Fletcher, Zakria Hussain, John Shawe-Taylor
2010	—	`conf`	Multivariate Bandits and Their Applications. IIP John Shawe-Taylor
2010	—	`conf`	Pinview: Implicit Feedback in Content-Based Image Retrieval. WAPA Peter Auer, Zakria Hussain, Samuel Kaski, Arto Klami, Jussi Kujala, Jorma Laaksonen, Alex Po Leung, Kitsuchart Pasupa, John Shawe-Taylor
2010	—	`ch.`	Prediction with the SVM Using Test Point Margins. Data Mining Süreyya Özögür-Akyüz, Zakria Hussain, John Shawe-Taylor
2010	—	`conf`	Preface. WAPA Tom Diethe, Nello Cristianini, John Shawe-Taylor
2010	—	`ed.`	Proceedings of the First Workshop on Applications of Pattern Analysis, WAPA 2010, Cumberland Lodge, Windsor, UK, September 1-3, 2010 WAPA Tom Diethe, Nello Cristianini, John Shawe-Taylor
2010	A	`conf`	Regret Bounds for Gaussian Process Bandit Problems. AISTATS Steffen Grünewälder, Jean-Yves Audibert, Manfred Opper, John Shawe-Taylor
2010	—	`conf`	Semi-supervised feature learning from clinical text. BIBM Zhuoran Wang, John Shawe-Taylor, Anoop D. Shah
2010	—	`conf`	Sensor placement and coordination via distributed multi-agent cooperative control. PETRA Alexandros Papangelis, Vangelis Metsis, John Shawe-Taylor, Fillia Makedon
2010	J	`jnl`	Sparse Semi-supervised Learning Using Conjugate Functions. J. Mach. Learn. Res. Shiliang Sun, John Shawe-Taylor
2009	J	`jnl`	Can eyes reveal interest? Implicit queries from gaze patterns. User Model. User Adapt. Interact. Antti Ajanki, David R. Hardoon, Samuel Kaski, Kai Puolamäki, John Shawe-Taylor
2009	J	`jnl`	Convergence analysis of kernel Canonical Correlation Analysis: theory and practice. Mach. Learn. David R. Hardoon, John Shawe-Taylor
2009	J	`jnl`	Efficient Sparse Kernel Feature Extraction Based on Partial Least Squares. IEEE Trans. Pattern Anal. Mach. Intell. Charanpal Dhanjal, Steve R. Gunn, John Shawe-Taylor
2009	J	`jnl`	GLM and SVM analyses of neural response to tonal and atonal stimuli: new techniques and a comparison. Connect. Sci. Simon Durrant, David R. Hardoon, André Brechmann, John Shawe-Taylor, Eduardo R. Miranda, Henning Scheich
2009	J	`jnl`	GLM and SVM analyses of neural response to tonal and atonal stimuli: new techniques and a comparison. Connect. Sci. Simon Durrant, David R. Hardoon, André Brechmann, John Shawe-Taylor, Eduardo R. Miranda, Henning Scheich
2009	J	`jnl`	Guest editors' introduction: Special Issue from ECML PKDD 2009. Mach. Learn. Aleksander Kolcz, Dunja Mladenic, Wray L. Buntine, Marko Grobelnik, John Shawe-Taylor
2009	J	`jnl`	Guest editors' introduction: special issue of selected papers from ECML PKDD 2009. Data Min. Knowl. Discov. Aleksander Kolcz, Dunja Mladenic, Wray L. Buntine, Marko Grobelnik, John Shawe-Taylor
2009	—	`conf`	Improving the Confidence of Machine Translation Quality Estimates. MTSummit Lucia Specia, Marco Turqui, Zhuoran Wang, John Shawe-Taylor, Craig Saunders
2009	A	`conf`	Large-Margin Structured Prediction via Linear Programming. AISTATS Zhuoran Wang, John Shawe-Taylor
2009	—	`conf`	Large-margin structural prediction via linear programming. SMART@EAMT Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák
2009	—	`ed.`	Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I ECML/PKDD (1) Wray L. Buntine, Marko Grobelnik, Dunja Mladenic, John Shawe-Taylor
2009	—	`ed.`	Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II ECML/PKDD (2) Wray L. Buntine, Marko Grobelnik, Dunja Mladenic, John Shawe-Taylor
2009	A	`conf`	Matching Pursuit Kernel Fisher Discriminant Analysis. AISTATS Tom Diethe, Zakria Hussain, David R. Hardoon, John Shawe-Taylor
2009	A	`conf`	PAC-Bayes Analysis Of Maximum Entropy Classification. AISTATS John Shawe-Taylor, David R. Hardoon
2009	J	`jnl`	Pattern analysis for the prediction of fungal pro-peptide cleavage sites. Discret. Appl. Math. Süreyya Özögür-Akyüz, John Shawe-Taylor, Gerhard-Wilhelm Weber, Z. B. Ögel
2009	B	`conf`	Prior Knowledge in Learning Finite Parameter Spaces. FG Dorota Glowacka, Louis Dorard, Alan Medlar, John Shawe-Taylor
2009	—	`conf`	Sentence-level confidence estimation for MT. SMART@EAMT Lucia Specia, Nicola Cancedda, Marc Dymetman, Craig Saunders, Marco Turchi, Nello Cristianini, Zhuoran Wang, John Shawe-Taylor
2009	J	`jnl`	Technical perspective - Machine learning for complex predictions. Commun. ACM John Shawe-Taylor
2008	—	`conf`	Kernel Regression Framework for Machine Translation: UCL System Description for WMT 2008 Shared Translation Task. WMT@ACL Zhuoran Wang, John Shawe-Taylor
2008	J	`jnl`	Responsive listening behavior. Comput. Animat. Virtual Worlds Marco Gillies, Xueni Pan, Mel Slater, John Shawe-Taylor
2008	—	`conf`	Theory of matching pursuit. NIPS Zakria Hussain, John Shawe-Taylor
2008	J	`jnl`	Using string kernels to identify famous performers from their playing style. Intell. Data Anal. Craig Saunders, David R. Hardoon, John Shawe-Taylor, Gerhard Widmer
2007	A	`conf`	A Framework for Probability Density Estimation. AISTATS John Shawe-Taylor, Alexander N. Dolia
2007	J	`jnl`	Advanced learning algorithms for cross-language patent retrieval and classification. Inf. Process. Manag. Yaoyong Li, John Shawe-Taylor
2007	B	`ed.`	Advances in Intelligent Data Analysis VII, 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007, Proceedings IDA Michael R. Berthold, John Shawe-Taylor, Nada Lavrac
2007	A*	`conf`	Approximate maximum margin algorithms with rules controlled by the number of mistakes. ICML Petroula Tsampouka, John Shawe-Taylor
2007	J	`jnl`	Complexity of pattern classes and the Lipschitz property. Theor. Comput. Sci. Amiran Ambroladze, Emilio Parrado-Hernández, John Shawe-Taylor
2007	—	`conf`	Gaussian Process Approximations of Stochastic Differential Equations. Gaussian Processes in Practice Cédric Archambeau, Dan Cornford, Manfred Opper, John Shawe-Taylor
2007	A	`conf`	Information Retrieval by Inferring Implicit Queries from Eye Movements. AISTATS David R. Hardoon, John Shawe-Taylor, Antti Ajanki, Kai Puolamäki, Samuel Kaski
2007	—	`conf`	Kernel Regression Based Machine Translation. HLT-NAACL (Short Papers) Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák
2007	J	`jnl`	Kernel ellipsoidal trimming. Comput. Stat. Data Anal. Alexander N. Dolia, Christopher J. Harris, John Shawe-Taylor, D. Mike Titterington
2007	A	`conf`	Margin based Transductive Graph Cuts using Linear Programming. AISTATS Kristiaan Pelckmans, John Shawe-Taylor, Johan A. K. Suykens, Bart De Moor
2007	—	`conf`	New feature selection frameworks in emotion recognition to evaluate the informative power of speech related features. ISSPA Halis Altun, John Shawe-Taylor, Gökhan Polat
2007	J	`jnl`	Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data. J. Mach. Learn. Res. Zakria Hussain, François Laviolette, Mario Marchand, John Shawe-Taylor, S. Charles Brubaker, Matthew D. Mullin
2007	J	`jnl`	Synthesis of maximum margin and multiview learning using unlabeled data. Neurocomputing Sándor Szedmák, John Shawe-Taylor
2007	J	`jnl`	Unsupervised analysis of fMRI data using kernel canonical correlation. NeuroImage David R. Hardoon, Janaina Mourão Miranda, Michael J. Brammer, John Shawe-Taylor
2007	—	`conf`	Using Generalization Error Bounds to Train the Set Covering Machine. ICONIP (1) Zakria Hussain, John Shawe-Taylor
2007	—	`conf`	Using Image Stimuli to Drive fMRI Analysis. ICONIP (1) David R. Hardoon, Janaina Mourão Miranda, Michael J. Brammer, John Shawe-Taylor
2007	—	`conf`	Variational Inference for Diffusion Processes. NIPS Cédric Archambeau, Manfred Opper, Yuan Shen, Dan Cornford, John Shawe-Taylor
2006	C	`conf`	A Correlation Approach for Automatic Image Annotation. ADMA David R. Hardoon, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
2006	A*	`conf`	A probabilistic model for text kernels. ICML Alain D. Lehmann, John Shawe-Taylor
2006	—	`conf`	Constant Rate Approximate Maximum Margin Algorithms. ECML Petroula Tsampouka, John Shawe-Taylor
2006	J	`jnl`	Kernel-Based Learning of Hierarchical Multilabel Classification Models. J. Mach. Learn. Res. Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
2006	—	`ed.`	Subspace, Latent Structure and Feature Selection, Statistical and Optimization, Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers SLSFS Craig Saunders, Marko Grobelnik, Steve R. Gunn, John Shawe-Taylor
2006	B	`conf`	Synthesis of maximum margin and multiview learning using unlabeled data. ESANN Sándor Szedmák, John Shawe-Taylor
2006	—	`conf`	The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces. ECML Alexander N. Dolia, Tijl De Bie, Christopher J. Harris, John Shawe-Taylor, D. M. Titterington
2006	—	`conf`	Tighter PAC-Bayes Bounds. NIPS Amiran Ambroladze, Emilio Parrado-Hernández, John Shawe-Taylor
2006	J	`jnl`	Using KCCA for Japanese-English cross-language information retrieval and document classification. J. Intell. Inf. Syst. Yaoyong Li, John Shawe-Taylor
2005	—	`conf`	An Investigation of Feature Models for Music Genre Classification Using the Support Vector Classifier. ISMIR Anders Meng, John Shawe-Taylor
2005	—	`conf`	Analysis of Generic Perceptron-Like Large Margin Classifiers. ECML Petroula Tsampouka, John Shawe-Taylor
2005	J	`jnl`	Comparison and fusion of multiresolution features for texture classification. Pattern Recognit. Lett. Shutao Li, John Shawe-Taylor
2005	J	`jnl`	Efficient Computation of Gapped Substring Kernels on Large Alphabets. J. Mach. Learn. Res. Juho Rousu, John Shawe-Taylor
2005	A*	`conf`	Learning hierarchical multi-category text classification models. ICML Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor
2005	B	`conf`	Mixture of Vector Experts. ALT Matthew Henderson, John Shawe-Taylor, Janez Zerovnik
2005	J	`jnl`	On the eigenspectrum of the gram matrix and the generalization error of kernel-PCA. IEEE Trans. Inf. Theory John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz S. Kandola
2005	J	`jnl`	PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification. Mach. Learn. Thore Graepel, Ralf Herbrich, John Shawe-Taylor
2005	—	`conf`	The 2005 PASCAL Visual Object Classes Challenge. MLCW Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Tom Griffiths, Frédéric Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-Taylor, Amos J. Storkey, Sándor Szedmák, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang
2005	—	`conf`	Two view learning: SVM-2K, Theory and Practice. NIPS Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, Sándor Szedmák
2004	J	`jnl`	Canonical Correlation Analysis: An Overview with Application to Learning Methods. Neural Comput. David R. Hardoon, Sándor Szedmák, John Shawe-Taylor
2004	B	`conf`	Complexity of Pattern Classes and Lipschitz Property. ALT Amiran Ambroladze, John Shawe-Taylor
2004	—	`book`	Kernel Methods for Pattern Analysis. John Shawe-Taylor, Nello Cristianini
2004	—	`book`	Kernel Methods for Pattern Analysis. John Shawe-Taylor, Nello Cristianini
2004	A*	`ed.`	Learning Theory, 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004, Proceedings COLT John Shawe-Taylor, Yoram Singer
2004	—	`conf`	Support Vector Machine to Synthesise Kernels. Deterministic and Statistical Methods in Machine Learning Hongying Meng, John Shawe-Taylor, Sándor Szedmák, Jason D. R. Farquhar
2004	—	`conf`	Texture Classification by Combining Wavelet and Contourlet Features. SSPR/SPR Shutao Li, John Shawe-Taylor
2004	—	`conf`	Using String Kernels to Identify Famous Performers from Their Playing Style. ECML Craig Saunders, David R. Hardoon, John Shawe-Taylor, Gerhard Widmer
2003	A*	`conf`	Linear Programming Boosting for Uneven Datasets. ICML Jure Leskovec, John Shawe-Taylor
2003	A*	`conf`	Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming. COLT Jaz S. Kandola, Thore Graepel, John Shawe-Taylor
2003	A	`conf`	Refining Kernels for Regression and Uneven Classification Problems. AISTATS Jaz S. Kandola, John Shawe-Taylor
2003	—	`conf`	Semi-Definite Programming by Perceptron Learning. NIPS Thore Graepel, Ralf Herbrich, Andriy Kharechko, John Shawe-Taylor
2003	C	`conf`	The SVM With Uneven Margins and Chinese Document Categorization. PACLIC Yaoyong Li, John Shawe-Taylor
2003	A*	`conf`	The Set Covering Machine with Data-Dependent Half-Spaces. ICML Mario Marchand, Mohak Shah, John Shawe-Taylor, Marina Sokolova
2003	A*	`conf`	When Is Small Beautiful? COLT Amiran Ambroladze, John Shawe-Taylor
2002	J	`jnl`	Boosting strategy for classification. Intell. Data Anal. Huma Lodhi, Grigoris I. Karakoulas, John Shawe-Taylor
2002	J	`jnl`	Covering numbers for support vector machines. IEEE Trans. Inf. Theory Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson
2002	—	`conf`	Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis. NIPS Alexei Vinokourov, John Shawe-Taylor, Nello Cristianini
2002	—	`conf`	Kernel Methods for Document Filtering. TREC Nicola Cancedda, Cyril Goutte, Jean-Michel Renders, Nicolò Cesa-Bianchi, Alex Conconi, Yaoyong Li, John Shawe-Taylor, Alexei Vinokourov, Thore Graepel, Claudio Gentile
2002	J	`jnl`	Latent Semantic Kernels. J. Intell. Inf. Syst. Nello Cristianini, John Shawe-Taylor, Huma Lodhi
2002	—	`conf`	Learning Semantic Similarity. NIPS Jaz S. Kandola, John Shawe-Taylor, Nello Cristianini
2002	J	`jnl`	Linear Programming Boosting via Column Generation. Mach. Learn. Ayhan Demiriz, Kristin P. Bennett, John Shawe-Taylor
2002	B	`conf`	On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum. ALT John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz S. Kandola
2002	B	`conf`	On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum. Discovery Science John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz S. Kandola
2002	J	`jnl`	On the generalization of soft margin algorithms. IEEE Trans. Inf. Theory John Shawe-Taylor, Nello Cristianini
2002	—	`conf`	PAC-Bayes & Margins. NIPS John Langford, John Shawe-Taylor
2002	—	`conf`	String Kernels, Fisher Kernels and Finite State Automata. NIPS Craig Saunders, John Shawe-Taylor, Alexei Vinokourov
2002	A*	`conf`	Syllables and other String Kernel Extensions. ICML Craig Saunders, Hauke Tschach, John Shawe-Taylor
2002	J	`jnl`	Text Classification using String Kernels. J. Mach. Learn. Res. Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini, Christopher J. C. H. Watkins
2002	—	`conf`	The Decision List Machine. NIPS Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe-Taylor
2002	A*	`conf`	The Perceptron Algorithm with Uneven Margins. ICML Yaoyong Li, Hugo Zaragoza, Ralf Herbrich, John Shawe-Taylor, Jaz S. Kandola
2002	J	`jnl`	The Set Covering Machine. J. Mach. Learn. Res. Mario Marchand, John Shawe-Taylor
2002	—	`conf`	The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum. NIPS John Shawe-Taylor, Christopher K. I. Williams
2002	J	`jnl`	Wanda: a database of duplicated fish genes. Nucleic Acids Res. Yves Van de Peer, John Shawe-Taylor, Jayabalan Joseph, Axel Meyer
2001	J	`jnl`	An Unsupervised Neural Network Approach to Profiling the Behavior of Mobile Phone Users for Use in Fraud Detection. J. Parallel Distributed Comput. Peter Burge, John Shawe-Taylor
2001	A*	`conf`	Composite Kernels for Hypertext Categorisation. ICML Thorsten Joachims, Nello Cristianini, John Shawe-Taylor
2001	J	`jnl`	Estimating the Support of a High-Dimensional Distribution. Neural Comput. Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alexander J. Smola, Robert C. Williamson
2001	A*	`conf`	Latent Semantic Kernels. ICML Nello Cristianini, John Shawe-Taylor, Huma Lodhi
2001	A*	`conf`	Learning with the Set Covering Machine. ICML Mario Marchand, John Shawe-Taylor
2001	J	`jnl`	Neural Network Learning: Theoretical Foundation. AI Mag. John Shawe-Taylor
2001	—	`conf`	On Kernel-Target Alignment. NIPS Nello Cristianini, John Shawe-Taylor, André Elisseeff, Jaz S. Kandola
2001	—	`conf`	On the Concentration of Spectral Properties. NIPS John Shawe-Taylor, Nello Cristianini, Jaz S. Kandola
2001	—	`conf`	Spectral Kernel Methods for Clustering. NIPS Nello Cristianini, John Shawe-Taylor, Jaz S. Kandola
2000	A*	`conf`	A Column Generation Algorithm For Boosting. ICML Kristin P. Bennett, Ayhan Demiriz, John Shawe-Taylor
2000	—	`book`	An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Nello Cristianini, John Shawe-Taylor
2000	C	`conf`	Boosting the Margin Distribution. IDEAL Huma Lodhi, Grigoris I. Karakoulas, John Shawe-Taylor
2000	J	`jnl`	Characterizing Graph Drawing with Eigenvectors. J. Chem. Inf. Comput. Sci. Tomaz Pisanski, John Shawe-Taylor
2000	A*	`conf`	Direct Bayes Point Machines. ICML Matthias Rychetsky, John Shawe-Taylor, Manfred Glesner
2000	J	`jnl`	Enlarging the Margins in Perceptron Decision Trees. Mach. Learn. Kristin P. Bennett, Nello Cristianini, John Shawe-Taylor, Donghui Wu
2000	A*	`conf`	Generalisation Error Bounds for Sparse Linear Classifiers. COLT Thore Graepel, Ralf Herbrich, John Shawe-Taylor
2000	C	`conf`	Graph Colouring by Maximal Evidence Edge Adding. PATAT Barry Rising, John Shawe-Taylor, Janez Zerovnik
2000	A*	`conf`	Sparsity vs. Large Margins for Linear Classifiers. COLT Ralf Herbrich, Thore Graepel, John Shawe-Taylor
2000	—	`conf`	Text Classification using String Kernels. NIPS Huma Lodhi, John Shawe-Taylor, Nello Cristianini, Christopher J. C. H. Watkins
1999	B	`conf`	A multiplicative updating algorithm for training support vector machine. ESANN Nello Cristianini, Colin Campbell, John Shawe-Taylor
1999	A*	`conf`	Covering Numbers for Support Vector Machines. COLT Ying Guo, Peter L. Bartlett, John Shawe-Taylor, Robert C. Williamson
1999	J	`jnl`	Detection of fraud in mobile telecommunications. Inf. Secur. Tech. Rep. John Shawe-Taylor, Keith Howker, Peter Burge
1999	J	`jnl`	Detection of fraud in mobile telecommunications. Inf. Secur. Tech. Rep. John Shawe-Taylor, Keith Howker, Peter Burge
1999	A*	`conf`	Further Results on the Margin Distribution. COLT John Shawe-Taylor, Nello Cristianini
1999	—	`conf`	Generalization Performance of Classifiers in Terms of Observed Covering Numbers. EuroCOLT John Shawe-Taylor, Nello Cristianini
1999	J	`jnl`	Introducing the Special Issue of Machine Learning Selected from Papers Presented at the 1997 Conference on Computational Learning Theory, COLT'97. Mach. Learn. John Shawe-Taylor
1999	—	`conf`	Large Margin DAGs for Multiclass Classification. NIPS John C. Platt, Nello Cristianini, John Shawe-Taylor
1999	A*	`conf`	Large Margin Trees for Induction and Transduction. ICML Donghui Wu, Kristin P. Bennett, Nello Cristianini, John Shawe-Taylor
1999	—	`conf`	Margin Distribution Bounds on Generalization. EuroCOLT John Shawe-Taylor, Nello Cristianini
1999	—	`conf`	Support Vector Method for Novelty Detection. NIPS Bernhard Schölkopf, Robert C. Williamson, Alexander J. Smola, John Shawe-Taylor, John C. Platt
1999	—	`conf`	The Entropy Regularization Information Criterion. NIPS Alexander J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson
1998	—	`conf`	A Neural Accelerator for Graph Colouring Based on an Edge Adding Technique. NC Barry Rising, Max van Daalen, John Shawe-Taylor, Peter Burge, Janez Zerovnik
1998	A*	`conf`	Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space. ICML Nello Cristianini, John Shawe-Taylor, Peter Sykacek
1998	J	`jnl`	Classification Accuracy Based on Observed Margin. Algorithmica John Shawe-Taylor
1998	—	`conf`	Dynamically Adapting Kernels in Support Vector Machines. NIPS Nello Cristianini, Colin Campbell, John Shawe-Taylor
1998	—	`conf`	Optimizing Classifers for Imbalanced Training Sets. NIPS Grigoris I. Karakoulas, John Shawe-Taylor
1998	J	`jnl`	Special Issue of DAM on the Vapnik-chervonenkis Dimension. Discret. Appl. Math. John Shawe-Taylor
1998	J	`jnl`	Structural Risk Minimization Over Data-Dependent Hierarchies. IEEE Trans. Inf. Theory John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony
1997	A*	`conf`	A PAC Analysis of a Bayesian Estimator. COLT John Shawe-Taylor, Robert C. Williamson
1997	J	`jnl`	A Sufficient Condition for Polynomial Distribution-dependent Learnability. Discret. Appl. Math. Martin Anthony, John Shawe-Taylor
1997	—	`conf`	Confidence Estimates of Classification Accuracy on New Examples. EuroCOLT John Shawe-Taylor
1997	—	`conf`	Data-Dependent Structural Risk Minimization for Perceptron Decision Trees. NIPS John Shawe-Taylor, Nello Cristianini
1997	B	`conf`	Parallel Graph colouring using FPGAs. FPL Barry Rising, Max van Daalen, Peter Burge, John Shawe-Taylor
1996	A*	`conf`	A Framework for Structural Risk Minimisation. COLT John Shawe-Taylor, Peter L. Bartlett, Robert C. Williamson, Martin Anthony
1996	—	`conf`	A recurrent network with stochastic weights. ICNN Jieyu Zhao, John Shawe-Taylor
1996	J	`jnl`	A unifying framework for invariant pattern recognition. Pattern Recognit. Lett. Jeffrey Wood, John Shawe-Taylor
1996	J	`jnl`	Fast String Matching in Stationary Ergodic Sources. Comb. Probab. Comput. John Shawe-Taylor
1996	J	`jnl`	Learning in Stochastic Bit Stream Neural Networks. Neural Networks Jieyu Zhao, John Shawe-Taylor, Max van Daalen
1996	B	`conf`	Learning to Compress Ergodic Sources. Data Compression Conference Jonathan Baxter, John Shawe-Taylor
1996	J	`jnl`	Representation Theory and Invariant Neural Networks. Discret. Appl. Math. Jeffrey Wood, John Shawe-Taylor
1996	J	`jnl`	Valid Generalisation from Approximate Interpolation. Comb. Probab. Comput. Martin Anthony, Peter L. Bartlett, Yuval Ishai, John Shawe-Taylor
1995	—	`conf`	Generalisation Analysis for Classes of Continuous Neural Networks. ICNN John Shawe-Taylor
1995	—	`conf`	Generalisation of A Class of Continuous Neural Networks. NIPS John Shawe-Taylor, Jieyu Zhao
1995	B	`conf`	Neural networks for invariant pattern recognition. ESANN Jeffrey Wood, John Shawe-Taylor
1995	J	`jnl`	On Specifying Boolean Functions by Labelled Examples. Discret. Appl. Math. Martin Anthony, Graham R. Brightwell, John Shawe-Taylor
1995	A*	`conf`	Sample Sizes for Sigmoidal Neural Networks. COLT John Shawe-Taylor
1995	J	`jnl`	Sample Sizes for Threshold Networks with Equivalences Inf. Comput. John Shawe-Taylor
1995	B	`conf`	The Complexity of Learning Minor Closed Graph Classes. ALT Carlos Domingo, John Shawe-Taylor
1994	J	`jnl`	A Result of Vapnik with Applications. Discret. Appl. Math. Martin Anthony, John Shawe-Taylor
1994	J	`jnl`	Coverings of complete bipartite graphs and associated structures. Discret. Math. John Shawe-Taylor
1994	J	`jnl`	Fast String Matching using an Softw. Pract. Exp. Jong Yong Kim, John Shawe-Taylor
1994	J	`jnl`	Generating binary sequences for stochastic computing. IEEE Trans. Inf. Theory Peter Jeavons, David A. Cohen, John Shawe-Taylor
1994	J	`jnl`	Homeomorphism of 2-Complexes is Graph Isomorphism Complete. SIAM J. Comput. John Shawe-Taylor, Tomaz Pisanski
1994	A	`conf`	Molecular Graph Eigenvectors for Molecular Coordinates. GD Patrick W. Fowler, Tomaz Pisanski, John Shawe-Taylor
1994	—	`ed.`	Proceedings of the First European Conference on Computational Learning Theory, EuroCOLT 1993, London, UK, December 20-22, 1993. EuroCOLT John Shawe-Taylor, Martin Anthony
1993	J	`jnl`	A Result of Vapnik with Applications. Discret. Appl. Math. Martin Anthony, John Shawe-Taylor
1993	J	`jnl`	Bounding Sample Size with the Vapnik-Chervonenkis Dimension. Discret. Appl. Math. John Shawe-Taylor, Martin Anthony, Norman Biggs
1993	J	`jnl`	Symmetries and discriminability in feedforward network architectures. IEEE Trans. Neural Networks John Shawe-Taylor
1993	J	`jnl`	Using the Perceptron Algorithm to Find Consistent Hypotheses. Comb. Probab. Comput. Martin Anthony, John Shawe-Taylor
1993	—	`conf`	Valid generalisation of functions from close approximations on a sample. EuroCOLT Martin Anthony, John Shawe-Taylor
1992	J	`jnl`	An Approximate String-Matching Algorithm. Theor. Comput. Sci. Jong Yong Kim, John Shawe-Taylor
1992	J	`jnl`	Classes of feedforward neural networks and their circuit complexity. Neural Networks John Shawe-Taylor, Martin Anthony, Walter Kern
1992	B	`conf`	Fast Multiple Keyword Searching. CPM Jong Yong Kim, John Shawe-Taylor
1992	A*	`conf`	On Exact Specification by Examples. COLT Martin Anthony, Graham R. Brightwell, David A. Cohen, John Shawe-Taylor
1991	—	`conf`	Threshold Network Learning in the Presence of Equivalences. NIPS John Shawe-Taylor
1990	J	`jnl`	Linear programming algorithm for neural networks. Neural Networks John Shawe-Taylor, David A. Cohen
1990	A*	`conf`	The Learnability of Formal Concepts. COLT Martin Anthony, Norman Biggs, John Shawe-Taylor
1988	J	`jnl`	Transformational theory of feedforward neural networks. Neural Networks David A. Cohen, C. Mannion, John Shawe-Taylor
1987	J	`jnl`	Automorphism Groups of Primitive Distance-Bitransitive Graphs are Almost Simple. Eur. J. Comb. John Shawe-Taylor
1987	J	`jnl`	Distance-regularised graphs are distance-regular or distance-biregular. J. Comb. Theory B Chris D. Godsil, John Shawe-Taylor
1987	J	`jnl`	Information and its Relation to Formalisms for the Complexities of the Real World. J. Inf. Technol. John Shawe-Taylor
1985	J	`jnl`	Distance-biregular graphs with 2-valent vertices and distance-regular line graphs. J. Comb. Theory B Bojan Mohar, John Shawe-Taylor
1983	J	`jnl`	Edge-colorability of graph bundles. J. Comb. Theory B Tomaz Pisanski, John Shawe-Taylor, Joze Vrabec
1981	J	`jnl`	Search for minimal trivalent cycle permutation graphs with girth nine. Discret. Math. Tomaz Pisanski, John Shawe-Taylor