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PEEK: A Large Dataset of Learner Engagement with Educational Videos


Sep 03, 2021
Sahan Bulathwela, Maria Perez-Ortiz, Erik Novak, Emine Yilmaz, John Shawe-Taylor

* To be published at ORSUM '21: 4th Workshop on Online Recommender Systems and User Modeling at ACM RecSys 2021 

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A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings


Dec 21, 2020
Théophile Cantelobre, Benjamin Guedj, María Pérez-Ortiz, John Shawe-Taylor

* 38 pages 

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Upper and Lower Bounds on the Performance of Kernel PCA


Dec 18, 2020
Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor

* 27 pages 

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VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement


Nov 02, 2020
Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, John Shawe-Taylor


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Tighter risk certificates for neural networks


Aug 12, 2020
María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári

* Preprint under review 

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PAC-Bayes Analysis Beyond the Usual Bounds


Jun 23, 2020
Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvari, John Shawe-Taylor

* Enhanced version of the paper with the same title presented at the NeurIPS 2019 Workshop on Machine Learning with Guarantees. Important update: the PAC-Bayes bound for unbounded loss functions (Section 2.3) is new 

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PAC-Bayes unleashed: generalisation bounds with unbounded losses


Jun 12, 2020
Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor


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Predicting Engagement in Video Lectures


Jun 10, 2020
Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani, Emine Yilmaz, John Shawe-Taylor

* In Proceedings of International Conference on Educational Data Mining 2020 

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Correlated Feature Selection with Extended Exclusive Group Lasso


Feb 27, 2020
Yuxin Sun, Benny Chain, Samuel Kaski, John Shawe-Taylor

* 10 pages, 4 figures 

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Towards an Integrative Educational Recommender for Lifelong Learners


Dec 03, 2019
Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, John Shawe-Taylor

* In Proceedings of AAAI Conference on Artificial Intelligence 2020 

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TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources


Nov 21, 2019
Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz, John Shawe-Taylor

* In Proceedings of AAAI Conference on Artificial Intelligence 2020 

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Constructing Artificial Data for Fine-tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation


Oct 21, 2019
Gaurav Singh, Zahra Sabet, John Shawe-Taylor, James Thomas

* NLP 

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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


Jun 18, 2019
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

* 40 pages, 10 figures 

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Deep Learning Enhanced Extended Depth-of-Field for Thick Blood-Film Malaria High-Throughput Microscopy


Jun 18, 2019
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

* 10 pages, 4 figures 

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Perturbed Model Validation: A New Framework to Validate Model Relevance


May 27, 2019
Jie M. Zhang, Earl T. Barr, Benjamin Guedj, Mark Harman, John Shawe-Taylor

* 10 pages 

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Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection


Nov 26, 2018
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

* Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:cs/0101200 

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Faster Convergence & Generalization in DNNs


Oct 10, 2018
Gaurav Singh, John Shawe-Taylor

* 9 pages, 6 figures 

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Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding


Oct 02, 2018
Gaurav Singh, James Thomas, Iain J. Marshall, John Shawe-Taylor, Byron C. Wallace

* Accepted for Publication in EMNLP 2018 

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PAC-Bayes bounds for stable algorithms with instance-dependent priors


Aug 30, 2018
Omar Rivasplata, Emilio Parrado-Hernandez, John Shawe-Taylor, Shiliang Sun, Csaba Szepesvari

* 16 pages, discussion of theory and experiments in the main body, detailed proofs and experimental details in the appendices 

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Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression


Jun 28, 2018
Seth Nabarro, Tristan Fletcher, John Shawe-Taylor

* 12 pages, 5 figures, 2 tables 

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Empirical Risk Minimization under Fairness Constraints


Jun 26, 2018
Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, Massimiliano Pontil


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Adaptive Mechanism Design: Learning to Promote Cooperation


Jun 11, 2018
Tobias Baumann, Thore Graepel, John Shawe-Taylor


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Improving Active Learning in Systematic Reviews


Jan 29, 2018
Gaurav Singh, James Thomas, John Shawe-Taylor

* 10 pages, many figures 

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A Tutorial on Canonical Correlation Methods


Nov 07, 2017
Viivi Uurtio, João M. Monteiro, Jaz Kandola, John Shawe-Taylor, Delmiro Fernandez-Reyes, Juho Rousu

* ACM Computing Surveys, Vol. 50, No. 6, Article 95. Publication date: October 2017 
* 33 pages 

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Probabilistic map-matching using particle filters


Nov 29, 2016
Kira Kempinska, Toby Davies, John Shawe-Taylor

* Proceedings of GISRUK 2016 conference 

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Improved Particle Filters for Vehicle Localisation


Nov 15, 2016
Kira Kempinska, John Shawe-Taylor

* Presented as a poster at NIPS 2016 Time Series Workshop 

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Localized Lasso for High-Dimensional Regression


Oct 14, 2016
Makoto Yamada, Koh Takeuchi, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski


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