Search Results for author: Rafael Poyiadzi

Found 12 papers, 1 papers with code

The Weak Supervision Landscape

no code implementations30 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.

BIG-bench Machine Learning Navigate

Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models

no code implementations18 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.

Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach

no code implementations17 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.

Domain Generalisation for Apparent Emotional Facial Expression Recognition across Age-Groups

no code implementations18 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.

Facial Expression Recognition (FER)

Understanding surrogate explanations: the interplay between complexity, fidelity and coverage

no code implementations9 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.

On the overlooked issue of defining explanation objectives for local-surrogate explainers

no code implementations10 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.

Hypothesis Testing for Class-Conditional Label Noise

no code implementations3 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.

FACE: Feasible and Actionable Counterfactual Explanations

1 code implementation20 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).

Ordinal Regression as Structured Classification

no code implementations31 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.

Classification General Classification +1

Label Propagation for Learning with Label Proportions

no code implementations24 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.

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