activity
20112022
most citedFAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems

37 citations · 79 across the 11 of their papers we have counts for

collaborators

22 papers

cs.LG202237 cited

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…

cs.LG20213 cited

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…

cs.LG20213 cited

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…

cs.NI20211 cited

Self-Supervised WiFi-Based Activity Recognition

Hok-Shing Lau, Ryan McConville, Mohammud J. Bocus +2

Traditional approaches to activity recognition involve the use of wearable sensors or cameras in order to recognise human activities. In this work, we extract fine-grained physical…

cs.NI20214 cited

Self-play Learning Strategies for Resource Assignment in Open-RAN Networks

Xiaoyang Wang, Jonathan D Thomas, Robert J Piechocki +3

Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks, supporting network services with various Qo…

cs.LG2021

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…