Browse by UCL people
Group by: Type | Date
Number of items: 84.
Article
Belissent, N;
Peña, JM;
Mesías-Ruiz, GA;
Shawe-Taylor, J;
Pérez-Ortiz, M;
(2024)
Transfer and zero-shot learning for scalable weed detection and classification in UAV images.
Knowledge-Based Systems
, 292
, Article 111586. 10.1016/j.knosys.2024.111586.
|
 |
|
Best, K;
Oakes, T;
Heather, JM;
Shawe-Taylor, J;
Chain, B;
(2015)
Computational analysis of stochastic heterogeneity in PCR amplification efficiency revealed by single molecule barcoding.
Scientific Reports
, 5
, Article 14629. 10.1038/srep14629.
|
|
|
Brown, BJ;
Manescu, P;
Przybylski, AA;
Caccioli, F;
Oyinloye, G;
Elmi, M;
Shaw, MJ;
... Fernandez-Reyes, D; + view all
(2020)
Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa.
Scientific Reports
, 10
(1)
, Article 15918. 10.1038/s41598-020-72575-6.
|
|
|
Bulathwela, Sahan;
Pérez-Ortiz, María;
Holloway, Catherine;
Cukurova, Mutlu;
Shawe-Taylor, John;
(2024)
Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive Tools.
Sustainability
, 16
(2)
, Article 781. 10.3390/su16020781.
|
|
Bulathwela, Sahan;
Perez-Ortiz, Maria;
Yilmaz, Emine;
Shawe-Taylor, John;
(2022)
Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources.
Sustainability
, 14
(18)
, Article 11682. 10.3390/su141811682.
|
|
Chen, H;
Cheng, T;
Shawe-Taylor, J;
(2017)
A Balanced Route Design for Min-Max Multiple-Depot Rural Postman Problem (MMMDRPP): a police patrolling case.
International Journal of Geographical Information Science
10.1080/13658816.2017.1380201.
(In press).
|
|
|
Chisholm, S;
Stein, AB;
Jordan, NR;
Hubel, TM;
Shawe-Taylor, J;
Fearn, T;
McNutt, JW;
... Hailes, S; + view all
(2019)
Parsimonious test of dynamic interaction.
Ecology and Evolution
, 9
(4)
pp. 1654-1664.
10.1002/ece3.4805.
|
|
|
Cinelli, M;
Sun, Y;
Best, K;
Heather, JM;
Reich-Zeliger, S;
Shifrut, E;
Friedman, N;
... Chain, B; + view all
(2017)
Feature selection using a one dimensional naïve Bayes' classifier increases the accuracy of support vector machine classification of CDR3 repertoires.
Bioinformatics
, 33
(7)
pp. 951-955.
10.1093/bioinformatics/btw771.
|
|
Cousins, S;
Shawe-Taylor, J;
(2017)
High-probability minimax probability machines.
Machine Learning
, 106
(6)
pp. 863-886.
10.1007/s10994-016-5616-2.
|
|
|
Danemayer, Jamie;
Holloway, Cathy;
Cho, Youngjun;
Berthouze, Nadia;
Singh, Aneesha;
Bhot, William;
Dixon, Ollie;
... Shawe-Taylor, John; + view all
(2023)
Seeking information about assistive technology: Exploring current practices, challenges, and the need for smarter systems.
International Journal of Human-Computer Studies
, 177
, Article 103078. 10.1016/j.ijhcs.2023.103078.
|
 |
|
Donini, M;
Monteiro, JM;
Pontil, M;
Hahn, T;
Fallgatter, AJ;
Shawe-Taylor, J;
Mourão-Miranda, J;
(2019)
Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important.
Neuroimage
, 195
pp. 215-231.
10.1016/j.neuroimage.2019.01.053.
|
|
|
Grünewälder, S;
Broekhuis, F;
Macdonald, DW;
Wilson, AM;
McNutt, JW;
Shawe-Taylor, J;
Hailes, S;
(2012)
Movement activity based classification of animal behaviour with an application to data from cheetah (Acinonyx jubatus).
PLoS One
, 7
(11)
, Article e49120. 10.1371/journal.pone.0049120.
|
|
|
Haddouche, M;
Guedj, B;
Rivasplata, O;
Shawe-Taylor, J;
(2021)
PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses.
Entropy
, 23
(10)
, Article 1330. 10.3390/e23101330.
|
|
|
Haworth, J;
Cheng, T;
Shawe-Taylor, J;
Wang, J;
(2014)
Local online kernel ridge regression for forecasting of urban travel times.
Transportation Research Part C: Emerging Technologies
, 46
151 - 178.
10.1016/j.trc.2014.05.015.
|
|
|
Kempinska, K;
Longley, P;
Shawe-Taylor, J;
(2018)
Interactional regions in cities: making sense of flows across networked systems.
International Journal of Geographical Information Science
, 32
(7)
pp. 1348-1367.
10.1080/13658816.2017.1418878.
|
|
Law, T;
Shawe-Taylor, J;
(2017)
Practical Bayesian support vector regression for financial time series prediction and market condition change detection.
Quantitative Finance
, 17
(9)
pp. 1403-1416.
10.1080/14697688.2016.1267868.
|
|
|
Lehtinen, S;
Lees, J;
Bähler, J;
Shawe-Taylor, J;
Orengo, C;
(2015)
Gene Function Prediction from Functional Association Networks Using Kernel Partial Least Squares Regression.
PLoS One
, 10
(8)
, Article e0134668. 10.1371/journal.pone.0134668.
|
|
|
Manescu, P;
Shaw, MJ;
Elmi, M;
Neary-Zajiczek, L;
Claveau, R;
Pawar, V;
Kokkinos, I;
... Fernandez-Reyes, D; + view all
(2020)
Expert-Level Automated Malaria Diagnosis on Routine Blood Films with Deep Neural Networks.
American Journal of Hematology
, 95
(8)
pp. 883-891.
10.1002/ajh.25827.
|
|
|
Michie, S;
Thomas, J;
Johnston, M;
Mac Aonghusa, P;
Shawe-Taylor, J;
Kelly, MP;
Deleris, LA;
... West, R; + view all
(2017)
The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation.
Implementation Science
, 12
, Article 121. 10.1186/s13012-017-0641-5.
|
|
|
Michie, S;
Thomas, J;
Mac Aonghusa, P;
West, R;
Johnston, M;
Kelly, MP;
Shawe-Taylor, J;
... O'Mara-Eves, A; + view all
(2020)
The Human Behaviour-Change Project: An artificial intelligence system to answer questions about changing behaviour [version 1; peer review: not peer reviewed].
Wellcome Open Research
, 5
, Article 122. 10.12688/wellcomeopenres.15900.1.
|
|
|
Mihalik, Agoston;
Chapman, James;
Adams, Rick A;
Winter, Nils R;
Ferreira, Fabio S;
Shawe-Taylor, John;
Mourão-Miranda, Janaina;
(2022)
Canonical Correlation Analysis and Partial Least Squares for identifying brain-behaviour associations: a tutorial and a comparative study.
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
, 7
(11)
pp. 1055-1067.
10.1016/j.bpsc.2022.07.012.
|
|
Mihalik, A;
Ferreira, F;
Moutoussis, M;
Ziegler, G;
Adams, RA;
Rosa, MJ;
Prabhu, G;
... Mourao-Miranda, J; + view all
(2020)
Multiple hold-outs with stability: improving the generalizability of machine learning analyses of brain-behaviour relationships.
Biological Psychiatry
, 87
(4)
pp. 368-376.
10.1016/j.biopsych.2019.12.001.
|
|
Milighetti, M;
Shawe-Taylor, J;
Chain, B;
(2021)
Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes.
Frontiers in Physiology
, 12
, Article 730908. 10.3389/fphys.2021.730908.
|
|
|
Monteiro, JM;
Rao, A;
Shawe-Taylor, J;
Mourao-Miranda, J;
(2016)
A multiple hold-out framework for Sparse Partial Least Squares.
Journal of Neuroscience Methods
, 271
pp. 182-194.
10.1016/j.jneumeth.2016.06.011.
|
|
Oneto, L;
Donini, M;
Pontil, M;
Shawe-Taylor, J;
(2020)
Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy.
Neurocomputing
, 416
pp. 231-243.
10.1016/j.neucom.2019.12.137.
|
|
Pavisic, IM;
Firth, NF;
Parsons, S;
Martinez Rego, D;
Shakespeare, TJ;
Yong, KXX;
Slattery, CF;
... Primativo, S; + view all
(2017)
Eyetracking Metrics in Young Onset Alzheimer’s Disease: A Window into Cognitive Visual Functions.
Frontiers in Neurology
, 8
, Article 377. 10.3389/fneur.2017.00377.
|
|
|
Pérez-Ortiz, M;
Rivasplata, O;
Shawe-Taylor, J;
Szepesvári, C;
(2021)
Tighter risk certificates for neural networks.
Journal of Machine Learning Research
, 22
, Article 227.
|
|
Rondina, J;
Hahn, T;
de Oliveira, L;
Marquand, A;
Dresler, T;
Leitner, T;
Fallgatter, A;
... Mourao-Miranda, J; + view all
(2014)
SCoRS - a method based on stability for feature selection and mapping in neuroimaging.
IEEE Trans Med Imaging
, 33
(1)
pp. 85-98.
10.1109/TMI.2013.2281398.
|
|
|
Rosa, MJ;
Portugal, L;
Hahn, T;
Fallgatter, AJ;
Garrido, MI;
Shawe-Taylor, J;
Mourao-Miranda, J;
(2015)
Sparse network-based models for patient classification using fMRI.
Neuroimage
, 105
493 - 506.
10.1016/j.neuroimage.2014.11.021.
|
|
Rousu, J;
Agranoff, DD;
Sodeinde, O;
Shawe-Taylor, J;
Fernandez-Reyes, D;
(2013)
Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria.
PLoS Computational Biology
, 9
(4)
, Article e1003018. 10.1371/journal.pcbi.1003018.
|
|
|
Shawe-Taylor, J;
Cristianini, N;
(2002)
On the generalization of soft margin algorithms.
IEEE TRANSACTIONS ON INFORMATION THEORY
, 48
(10)
pp. 2721-2735.
10.1109/TIT.2002.802647.
|
|
|
Shawe-Taylor, J;
Williams, CKI;
Cristianini, N;
Kandola, J;
(2005)
On the eigenspectrum of the Gram matrix and the generalization error of kernel-PCA.
IEEE TRANSACTIONS ON INFORMATION THEORY
, 51
(7)
pp. 2510-2522.
10.1109/TIT.2005.850052.
|
|
|
Sun, Li;
Shawe-Taylor, John;
D'Ayala, Dina;
(2022)
Artificial intelligence-informed planning for the rapid response of hazard-impacted road networks.
Scientific Reports
, 12
(1)
, Article 16286. 10.1038/s41598-022-19637-z.
|
|
Sun, S;
Yu, M;
Shawe-Taylor, J;
Mao, L;
(2022)
Stability-based PAC-Bayes analysis for multi-view learning algorithms.
Information Fusion
, 86-87
pp. 76-92.
10.1016/j.inffus.2022.06.006.
|
|
Sun, S;
Shawe-Taylor, J;
(2010)
Sparse Semi-supervised Learning Using Conjugate Functions.
Journal of Machine Learning Research
, 11
2423 - 2455.
|
|
Sun, S;
Shawe-Taylor, J;
Mao, L;
(2017)
PAC-Bayes analysis of multi-view learning.
Information Fusion
, 35
pp. 117-131.
10.1016/j.inffus.2016.09.008.
|
|
Sun, Y;
Best, K;
Cinelli, M;
Heather, JM;
Reich-Zeliger, S;
Shifrut, E;
Friedman, N;
... Chain, B; + view all
(2017)
Specificity, Privacy, and Degeneracy in the CD4 T Cell Receptor Repertoire Following Immunization.
Front Immunol
, 8
, Article 430. 10.3389/fimmu.2017.00430.
|
|
|
Thomas, N;
Best, K;
Cinelli, M;
Reich-Zeliger, S;
Gal, H;
Shifrut, E;
Madi, A;
... Chain, B; + view all
(2014)
Tracking global changes induced in the CD4 T-cell receptor repertoire by immunization with a complex antigen using short stretches of CDR3 protein sequence.
Bioinformatics
, 30
(22)
3181 - 3188.
10.1093/bioinformatics/btu523.
|
|
|
Thomas, N;
Matejovicova, L;
Srikusalanukul, W;
Shawe-Taylor, J;
Chain, B;
(2012)
Directional migration of recirculating lymphocytes through lymph nodes via random walks.
PLoS One
, 7
(9)
, Article e45262. 10.1371/journal.pone.0045262.
|
|
|
Uurtio, V;
Monteiro, JM;
Kandola, J;
Shawe-Taylor, J;
Fernandez-Reyes, D;
Rousu, J;
(2018)
A Tutorial on Canonical Correlation Methods.
ACM Computing Surveys (CSUR)
, 50
(6)
, Article 95. 10.1145/3136624.
|
|
|
Wang, Z;
Shah, AD;
Tate, AR;
Denaxas, S;
Shawe-Taylor, J;
Hemingway, H;
(2012)
Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.
PLOS One
, 7
(1)
, Article e30412. 10.1371/journal.pone.0030412.
|
|
|
Zhang, JM;
Harman, M;
Guedj, B;
Barr, ET;
Shawe-Taylor, J;
(2023)
Model validation using mutated training labels: An exploratory study.
Neurocomputing
, 539
, Article 126116. 10.1016/j.neucom.2023.02.042.
|
Book
|
Cheng, T;
Bowers, K;
Longley, P;
Shawe-Taylor, J;
Trevor, A;
Davies, T;
Rosser, G;
+ view all
(2016)
CPC: Crime, Policing and Citizenship - Intelligent Policing and Big Data.
[Book].
(1st ed.).
UCL Space Lab: London, UK.
|
|
Book chapter
|
Bulathwela, S;
Pérez-Ortiz, M;
Yilmaz, E;
Shawe-Taylor, J;
(2023)
Leveraging Semantic Knowledge Graphs in Educational Recommenders to Address the Cold-Start Problem.
In:
Semantic AI in Knowledge Graphs.
(pp. 1-20).
CRC Press
|
Proceedings paper
|
Baumann, T;
Graepel, T;
Shawe-Taylor, J;
(2020)
Adaptive Mechanism Design: Learning to Promote Cooperation.
In:
Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN).
IEEE: Glasgow, UK.
|
|
Bulathwela, S;
Pérez-Ortiz, M;
Holloway, C;
Shawe-Taylor, J;
Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution.
In:
NeurIPS 2021 Workshop on Machine Learning for the Developing World (ML4D).
NeurIPS
(In press).
|
|
|
Bulathwela, S;
Pérez-Ortiz, M;
Yilmaz, E;
Shawe-Taylor, J;
(2022)
Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems.
In:
Proceedings of First International Workshop on Joint Use of Probabilistic Graphical Models and Ontology at Conference on Knowledge Graph and Semantic Web 2021.
|
|
Bulathwela, Sahan;
Perez-Ortiz, Maria;
Novak, Erik;
Yilmaz, Emine;
Shawe-Taylor, John;
(2021)
PEEK: A Large Dataset of Learner Engagement with Educational Videos.
In:
Proceedings of the 4th Workshop on Online Recommender Systems and User Modeling (ORSUM 2021), in conjunction with the 15th ACM Conference on Recommender Systems.
ORSUM: Amsterdam, The Netherlands.
|
|
Bulathwela, Sahan;
Verma, Meghana;
Perez-Ortiz, Maria;
Yilmaz, Emine;
Shawe-Taylor, John;
(2022)
Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments.
In:
Proceedings of the 15th International Conference on Educational Data Mining, July 2022.
(pp. pp. 414-421).
International Educational Data Mining Society
|
|
Bulathwela, S;
PAerez-Ortiz, M;
Yilmaz, E;
Shawe-Taylor, J;
(2020)
Towards an Integrative Educational Recommender for Lifelong Learners.
In:
Proceedings of the AAAI Conference on Artificial Intelligence.
(pp. pp. 13759-13760).
Association for the Advancement of Artificial Intelligence (AAAI)
|
|
|
Bulathwela, S;
Pérez-Ortiz, M;
Lipani, A;
Yilmaz, E;
Shawe-Taylor, J;
(2020)
Predicting Engagement in Video Lectures.
In:
Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020).
(pp. pp. 50-60).
Educational Data Mining (EDM)
|
|
Bulathwela, S;
Pérez-Ortiz, M;
Mehrotra, R;
Orlic, D;
De La Higuera, C;
Shawe-Taylor, J;
Yilmaz, E;
(2020)
SUM’20: State-based user modelling.
In:
WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining.
(pp. pp. 899-900).
ACM: Houston, TX, USA.
|
|
|
Bulathwela, S;
Pérez-Ortiz, M;
Yilmaz, E;
Shawe-Taylor, J;
(2020)
TrueLearn: A family of bayesian algorithms to match lifelong learners to open educational resources.
In:
AAAI Technical Track: Applications.
(pp. pp. 565-573).
AAAI
|
|
|
Donini, M;
Martinez-Rego, D;
Goodson, M;
Shawe-Taylor, J;
Pontil, M;
(2016)
Distributed variance regularized Multitask Learning.
In:
2016 International Joint Conference on Neural Networks (IJCNN).
(pp. pp. 3101-3109).
IEEE
|
|
|
Donini, M;
Monteiro, JM;
Pontil, M;
Shawe-Taylor, J;
Mourao-Miranda, J;
(2016)
A multimodal multiple kernel learning approach to Alzheimer's disease detection.
In:
Proceedings of the 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).
IEEE: Vietri sul Mare, Italy.
|
|
Donini, M;
Oneto, L;
Ben-David, S;
Shawe-Taylor, J;
Pontil, M;
(2018)
Empirical Risk Minimization Under Fairness Constraints.
In: Bengio, S and Wallach, H and Larochelle, H and Grauman, K and CesaBianchi, N and Garnett, R, (eds.)
Proceedings of the 32nd Conference on Neural Information Processing Systems.
Neural Information Processing Systems (NIPS): Montreal, Canada.
|
|
|
Ferreira, FS;
Rosa, MJ;
Moutoussis, M;
Dolan, R;
Shawe-Taylor, J;
Ashburner, J;
Mourao-Miranda, J;
(2018)
Sparse PLS hyper-parameters optimisation for investigating brain-behaviour relationships.
In:
2018 International Workshop on Pattern Recognition in Neuroimaging (PRNI).
IEEE
|
|
|
Kempinska, K;
Davies, T;
Shawe-Taylor, J;
(2017)
Probabilistic map-matching for low-frequency GPS trajectories.
In:
Proceedings of GIS Ostrava 2017: Dynamics in GIscience.
(pp. pp. 209-221).
Springer: Ostrava, Czech Republic.
|
|
|
Kempinska, KK;
Davies, T;
Shawe-Taylor, J;
(2016)
Probabilistic Map-matching using Particle Filters.
In:
Proceedings of 24th GIS Research UK (GISRUK 2016) Conference.
Greenwich GIS Research Group: London, UK.
|
|
Kreitmayer, S;
Rogers, Y;
Yilmaz, E;
Shawe-Taylor, J;
(2018)
Design in the Wild: Interfacing the OER learning journey.
In:
Proceedings of British HCI 2018. Belfast, UK.
(pp. p. 164).
ACM: Association for Computing Machinery: Belfast, UK.
|
|
|
Marchand, M;
Su, H;
Morvant, E;
Rousu, J;
Shawe-Taylor, J;
(2014)
Multilabel structured output learning with random spanning trees of max-margin Markov networks.
In: Ghahramani, Z and Welling, M and Cortes, C and Lawrence, ND and Weinberger, KQ, (eds.)
[NIPS 2014: Electronic Proceedings of the 25th Neural Information Processing Systems Conference].
|
|
|
Perez-Ortiz, M;
Rivasplata, O;
Parrado-Hernandez, E;
Guedj, B;
Shawe-Taylor, J;
(2021)
Progress in Self-Certified Neural Networks.
In:
Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021).
NeurIPS
|
|
|
Perez-Ortiz, Maria;
Bulathwela, Sahan;
Dormann, Claire;
Verma, Meghana;
Kreitmayer, Stefan;
Noss, Richard;
Shawe-Taylor, John;
... Yilmaz, Emine; + view all
(2022)
Watch Less and Uncover More: Could Navigation Tools Help Users Search and Explore Videos?
In:
CHIIR '22: Proceedings of the 2022 Conference on Human Information Interaction and Retrieval.
(pp. pp. 90-101).
ACM
|
|
|
Perez-Ortiz, M;
Dormann, C;
Rogers, Y;
Bulathwela, S;
Kreitmayer, S;
Yilmaz, E;
Noss, R;
(2021)
X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI.
In:
Proceedings of the 26th International Conference on Intelligent User Interfaces.
(pp. pp. 70-74).
ACM: Association for Computing Machinery: New York, NY, USA.
|
|
|
Rivasplata, O;
Kuzborskij, I;
Szepesvári, C;
Shawe-Taylor, J;
(2020)
PAC-Bayes analysis beyond the usual bounds.
In:
Advances in Neural Information Processing Systems 33 (NeurIPS 2020).
Neural Information Processing Systems (NeurIPS): Vancouver, Canada.
|
|
|
Rivasplata, O;
Parrado-Hernández, E;
Shawe-Taylor, J;
Sun, S;
Szepesvári, C;
(2018)
PAC-Bayes bounds for stable algorithms with instance-dependent priors.
In: Bengio, S and Wallach, HM and Larochelle, H and Grauman, K and Cesa-Bianchi, N, (eds.)
Proceedings of the 32nd International Conference on Neural Information Processing Systems - NIPS'18.
(pp. pp. 9234-9244).
Association for Computing Machinery (ACM): Montréal, Canada.
|
|
Rondina, JM;
Shawe-Taylor, J;
Mourao-Miranda, J;
(2013)
Stability-based multivariate mapping using SCoRS.
In: Davatzikos, C, (ed.)
3rd International Workshop on Pattern Recognition in Neuroimaging (PRNI 2013): Proceedings.
(pp. pp. 198-202).
IEEE
|
|
|
Rondina, JM;
Shawe-Taylor, J;
Mourão-Miranda, J;
(2012)
A new feature selection method based on stability theory - Exploring parameters space to evaluate classification accuracy in neuroimaging data.
In:
Machine Learning and Interpretation in Neuroimaging.
(pp. pp. 51-59).
Springer: Cham, Switzerland.
|
|
|
Schmitt, S;
Shawe-Taylor, J;
van Hasselt, H;
(2023)
Exploration via Epistemic Value Estimation.
In:
Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023.
(pp. pp. 9742-9751).
The Association for the Advancement of Artificial Intelligence (AAAI)
|
|
Schmitt, Simon;
Shawe-Taylor, John;
Van Hasselt, Hado;
(2022)
Chaining Value Functions for Off-Policy Learning.
In: Sycara, Katia, (ed.)
Proceedings of the AAAI 2022 Conference: The 36th AAAI Conference on Artificial Intelligence.
(pp. pp. 8187-8195).
Association for the Advancement of Artificial Intelligence (AAAI): Virtual conference.
|
|
Shawe-Taylor, J;
Zlicar, B;
(2015)
Novelty Detection with One-Class Support Vector Machines.
In: Morlini, I and Minerva, T and Vichi, M, (eds.)
Advances in Statistical Models for Data Analysis.
(pp. pp. 231-257).
Springer, Cham
|
|
|
Singh, G;
Sabet, Z;
Shawe-Taylor, J;
Thomas, J;
(2020)
Constructing Artificial Data for Fine-Tuning for Low-Resource Biomedical Text Tagging with Applications in PICO Annotation.
In:
Explainable AI in Healthcare and Medicine.
(pp. pp. 131-145).
Springer Nature: Cham, Switzerland.
|
|
Singh, G;
Marshall, IJ;
Thomas, J;
Shawe-Taylor, J;
Wallace, BC;
(2017)
A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation.
In:
(Proceedings) ACM Conference on Information and Knowledge Management (CIKM).
(pp. pp. 1519-1528).
ACM
|
|
|
Singh, G;
Thomas, J;
Marshall, IJ;
Shawe-Taylor, J;
Wallace, BC;
(2018)
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding.
In: Riloff, E and Chiang, D and Hockenmaier, J and Tsujii, J, (eds.)
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.
(pp. pp. 2837-2842).
Association for Computational Linguistics: Brussels, Belgium.
|
|
|
Wang, Z;
Shawe-Taylor, J;
Szedmak, S;
(2007)
Kernel regression based machine translation.
In:
(pp. pp. 185-188).
|
|
|
Yamada, J;
Shawe-Taylor, J;
Fountas, Z;
(2020)
Evolution of a Complex Predator-Prey Ecosystem on Large-scale Multi-Agent Deep Reinforcement Learning.
In:
Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN).
IEEE: Glasgow, UK.
|
|
|
Yamada, M;
Takeuchi, K;
Iwata, T;
Shawe-Taylor, J;
Kaski, S;
(2017)
Localized lasso for high-dimensional regression.
In:
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017.
PMLR: Fort Lauderdale, FL, USA.
|
|
Report
|
Perez-Ortiz, Maria;
Novak, Erik;
Bulathwela, Sahan;
Shawe-Taylor, John;
(2020)
An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All.
(Opinion Series Report
).
UNESCO; International Research Centre in Artificial Intelligence (IRCAI)
|
Working / discussion paper
|
Bulathwela, Sahan;
Perez-Ortiz, Maria;
Yilmaz, Emine;
Shawe-Taylor, John;
(2020)
VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement.
ArXiv
|
|
Cantelobre, Théophile;
Guedj, Benjamin;
Pérez-Ortiz, María;
Shawe-Taylor, John;
(2020)
A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings.
ArXiv
|
|
Haddouche, M;
Guedj, B;
Rivasplata, O;
Shawe-Taylor, J;
(2020)
Upper and Lower Bounds on the Performance of Kernel PCA.
arXiv: Ithaca, NY, USA.
|
|
|
Lee, F;
Perez Ortiz, M;
Shawe-Taylor, J;
(2021)
Computational modelling of COVID-19: A study of compliance and superspreaders.
MedRxiv: Cold Spring Harbor, NY, USA.
|
|
|
Michie, S;
Thomas, J;
Mac Aonghusa, P;
West, R;
Johnston, M;
Kelly, M;
Shawe-Taylor, J;
... O’Mara-Eves, A; + view all
(2020)
The Human Behaviour-Change Project: An artificial intelligence system to answer questions about changing behaviour.
Wellcome Open Research
|
|
|
Perez-Ortiz, Maria;
Rivasplata, Omar;
Guedj, Benjamin;
Gleeson, Matthew;
Zhang, Jingyu;
Shawe-Taylor, John;
Bober, Miroslaw;
(2021)
Learning PAC-Bayes Priors for Probabilistic Neural Networks.
ArXiv: Ithaca, NY, USA.
|
|
