no code implementations • 8 Sep 2022 • Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raul Santos-Rodriguez, Peter Flach
Predictive systems, in particular machine learning algorithms, can take important, and sometimes legally binding, decisions about our everyday life.
no code implementations • 30 Mar 2022 • Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesus Cid-Sueiro, Miquel Perello-Nieto, Peter Flach, Raul Santos-Rodriguez
In this paper we propose a framework for categorising weak supervision settings with the aim of: (1) helping the dataset owner or annotator navigate through the available options within weak supervision when prescribing an annotation process, and (2) describing existing annotations for a dataset to machine learning practitioners so that we allow them to understand the implications for the learning process.
no code implementations • 18 Mar 2022 • Niall Twomey, Sarah McMullan, Anat Elhalal, Rafael Poyiadzi, Luis Vaquero
At present, the educational data mining community lacks many tools needed for ensuring equitable ability estimation for Neurodivergent (ND) learners.
no code implementations • 17 Nov 2021 • Jonas Schulz, Rafael Poyiadzi, Raul Santos-Rodriguez
To this end, we produce estimates of the uncertainty of a given explanation by measuring the ordinal consensus amongst a set of diverse bootstrapped surrogate explainers.
no code implementations • 18 Oct 2021 • Rafael Poyiadzi, Jie Shen, Stavros Petridis, Yujiang Wang, Maja Pantic
We then study the effect of variety and number of age-groups used during training on generalisation to unseen age-groups and observe that an increase in the number of training age-groups tends to increase the apparent emotional facial expression recognition performance on unseen age-groups.
no code implementations • 9 Jul 2021 • Rafael Poyiadzi, Xavier Renard, Thibault Laugel, Raul Santos-Rodriguez, Marcin Detyniecki
This paper analyses the fundamental ingredients behind surrogate explanations to provide a better understanding of their inner workings.
no code implementations • 10 Jun 2021 • Rafael Poyiadzi, Xavier Renard, Thibault Laugel, Raul Santos-Rodriguez, Marcin Detyniecki
In this work we review the similarities and differences amongst multiple methods, with a particular focus on what information they extract from the model, as this has large impact on the output: the explanation.
no code implementations • 3 Mar 2021 • Rafael Poyiadzi, Weisong Yang, Niall Twomey, Raul Santos-Rodriguez
Differently, in this paper we assume we have access to a set of anchor points whose true posterior is approximately 1/2.
no code implementations • 3 Jul 2020 • Rafael Poyiadzi, Weisong Yang, Yoav Ben-Shlomo, Ian Craddock, Liz Coulthard, Raul Santos-Rodriguez, James Selwood, Niall Twomey
There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia.
1 code implementation • 20 Sep 2019 • Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez, Tijl De Bie, Peter Flach
First, a counterfactual example generated by the state-of-the-art systems is not necessarily representative of the underlying data distribution, and may therefore prescribe unachievable goals(e. g., an unsuccessful life insurance applicant with severe disability may be advised to do more sports).
no code implementations • 31 May 2019 • Niall Twomey, Rafael Poyiadzi, Callum Mann, Raúl Santos-Rodríguez
This paper extends the class of ordinal regression models with a structured interpretation of the problem by applying a novel treatment of encoded labels.
no code implementations • 24 Oct 2018 • Rafael Poyiadzi, Raul Santos-Rodriguez, Niall Twomey
Learning with Label Proportions (LLP) is the problem of recovering the underlying true labels given a dataset when the data is presented in the form of bags.