290 citations · 345 across the 14 of their papers we have counts for
15 papers
Evaluating Strategic Reasoning in Forecasting Agents
Tom Liptay, Dan Schwarz, Rafael Poyiadzi +2
Forecasting benchmarks produce accuracy leaderboards but little insight into why some forecasters are more accurate than others. We introduce Bench to the Future 2 (BTF-2), 1,417 p…
RAmBLA: A Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain
William James Bolton, Rafael Poyiadzi, Edward R. Morrell +2
Large Language Models (LLMs) increasingly support applications in a wide range of domains, some with potential high societal impact such as biomedicine, yet their reliability in re…
Hypothesis Testing for Class-Conditional Noise Using Local Maximum Likelihood
Weisong Yang, Rafael Poyiadzi, Niall Twomey +1
In supervised learning, automatically assessing the quality of the labels before any learning takes place remains an open research question. In certain particular cases, hypothesis…
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems
Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi +3
Predictive systems, in particular machine learning algorithms, can take important, and sometimes legally binding, decisions about our everyday life. In most cases, however, these s…
The Weak Supervision Landscape
Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesus Cid-Sueiro +3
Many ways of annotating a dataset for machine learning classification tasks that go beyond the usual class labels exist in practice. These are of interest as they can simplify or f…
Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models
Niall Twomey, Sarah McMullan, Anat Elhalal +2
At present, the educational data mining community lacks many tools needed for ensuring equitable ability estimation for Neurodivergent (ND) learners. On one hand, most learner mode…
Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach
Jonas Schulz, Rafael Poyiadzi, Raul Santos-Rodriguez
Explainability of black-box machine learning models is crucial, in particular when deployed in critical applications such as medicine or autonomous cars. Existing approaches produc…
Domain Generalisation for Apparent Emotional Facial Expression Recognition across Age-Groups
Rafael Poyiadzi, Jie Shen, Stavros Petridis +2
Apparent emotional facial expression recognition has attracted a lot of research attention recently. However, the majority of approaches ignore age differences and train a generic…
Understanding surrogate explanations: the interplay between complexity, fidelity and coverage
Rafael Poyiadzi, Xavier Renard, Thibault Laugel +2
This paper analyses the fundamental ingredients behind surrogate explanations to provide a better understanding of their inner workings. We start our exposition by considering glob…
On the overlooked issue of defining explanation objectives for local-surrogate explainers
Rafael Poyiadzi, Xavier Renard, Thibault Laugel +2
Local surrogate approaches for explaining machine learning model predictions have appealing properties, such as being model-agnostic and flexible in their modelling. Several method…
Hypothesis Testing for Class-Conditional Label Noise
Rafael Poyiadzi, Weisong Yang, Niall Twomey +1
In this paper we provide machine learning practitioners with tools to answer the question: is there class-conditional noise in my labels? In particular, we present hypothesis tests…
Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data
Rafael Poyiadzi, Weisong Yang, Yoav Ben-Shlomo +5
There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia. The emergence of state-of-the-art sensing platform…
FACE: Feasible and Actionable Counterfactual Explanations
Rafael Poyiadzi, Kacper Sokol, Raul Santos-Rodriguez +2
Work in Counterfactual Explanations tends to focus on the principle of "the closest possible world" that identifies small changes leading to the desired outcome. In this paper we a…
Ordinal Regression as Structured Classification
Niall Twomey, Rafael Poyiadzi, Callum Mann +1
This paper extends the class of ordinal regression models with a structured interpretation of the problem by applying a novel treatment of encoded labels. The net effect of this is…
Label Propagation for Learning with Label Proportions
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. This paradigm is parti…