Edwin Simpson
Cited by
Cited by
Dynamic bayesian combination of multiple imperfect classifiers
E Simpson, S Roberts, I Psorakis, A Smith
Decision making and imperfection, 1-35, 2013
A disaster response system based on human-agent collectives
SD Ramchurn, TD Huynh, F Wu, Y Ikuno, J Flann, L Moreau, JE Fischer, ...
Journal of Artificial Intelligence Research 57, 661-708, 2016
Space Warps – I. Crowdsourcing the discovery of gravitational lenses
PJ Marshall, A Verma, A More, CP Davis, S More, A Kapadia, M Parrish, ...
Monthly Notices of the Royal Astronomical Society 455 (2), 1171-1190, 2016
Text processing like humans do: Visually attacking and shielding NLP systems
S Eger, GG Şahin, A Rücklé, JU Lee, C Schulz, M Mesgar, K Swarnkar, ...
arXiv preprint arXiv:1903.11508, 2019
Content-centered collaboration spaces in the cloud
J Erickson, M Rhodes, S Spence, D Banks, J Rutherford, E Simpson, ...
IEEE Internet computing 13 (5), 34-42, 2009
Clustering tags in enterprise and web folksonomies
E Simpson
Proceedings of the International AAAI Conference on Web and Social Media 2 …, 2008
Predicting economic indicators from web text using sentiment composition
A Levenberg, S Pulman, K Moilanen, E Simpson, S Roberts
International Journal of Computer and Communication Engineering 3 (2), 109-115, 2014
Finding convincing arguments using scalable Bayesian preference learning
E Simpson, I Gurevych
Transactions of the Association for Computational Linguistics 6, 357-371, 2018
Bayesian methods for intelligent task assignment in crowdsourcing systems
E Simpson, S Roberts
Decision Making: Uncertainty, Imperfection, Deliberation and Scalability, 1-32, 2015
Language understanding in the wild: Combining crowdsourcing and machine learning
ED Simpson, M Venanzi, S Reece, P Kohli, J Guiver, SJ Roberts, ...
Proceedings of the 24th international conference on world wide web, 992-1002, 2015
Bayesian combination of multiple, imperfect classifiers
E Simpson, SJ Roberts, A Smith, C Lintott
University of Oxford, 2011
Scalable Bayesian preference learning for crowds
E Simpson, I Gurevych
Machine Learning 109 (4), 689-718, 2020
A Bayesian approach for sequence tagging with crowds
E Simpson, I Gurevych
arXiv preprint arXiv:1811.00780, 2018
Tag clustering with self organizing maps
ML Sbodio, E Simpson
HP Labs Techincal Reports, 2009
Predicting humorousness and metaphor novelty with Gaussian process preference learning
E Simpson, EL Do Dinh, T Miller, I Gurevych
Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019
Combined decision making with multiple agents
ED Simpson
University of Oxford, 2014
Economic Prediction using heterogeneous data streams from the World Wide Web
A Levenberg, E Simpson, S Roberts, G Gottlob
Scalable Decision Making: Uncertainty, Imperfection, Deliberation (SCALE …, 2013
Agent aptitude prediction
A Volkov, M Yankelevich, M Abramchik, A Levenberg
US Patent 11,080,608, 2021
Low resource sequence tagging with weak labels
E Simpson, J Pfeiffer, I Gurevych
Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 8862-8869, 2020
Interactive data analytics for the humanities
I Gurevych, CM Meyer, C Binnig, J Fürnkranz, K Kersting, S Roth, ...
International Conference on Computational Linguistics and Intelligent Text …, 2017
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