290 citations · 469 across the 35 of their papers we have counts for
19 papers · 1 filter
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse
Edward A. Small, Jeffrey N. Clark, Christopher J. McWilliams +4
Counterfactuals operationalised through algorithmic recourse have become a powerful tool to make artificial intelligence systems explainable. Conceptually, given an individual clas…
TraCE: Trajectory Counterfactual Explanation Scores
Jeffrey N. Clark, Edward A. Small, Nawid Keshtmand +5
Counterfactual explanations, and their associated algorithmic recourse, are typically leveraged to understand, explain, and potentially alter a prediction coming from a black-box c…
Privacy in Multimodal Federated Human Activity Recognition
Alex Iacob, Pedro P. B. Gusmão, Nicholas D. Lane +5
Human Activity Recognition (HAR) training data is often privacy-sensitive or held by non-cooperative entities. Federated Learning (FL) addresses such concerns by training ML models…
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…
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…
bLIMEy: Surrogate Prediction Explanations Beyond LIME
Kacper Sokol, Alexander Hepburn, Raul Santos-Rodriguez +1
Surrogate explainers of black-box machine learning predictions are of paramount importance in the field of eXplainable Artificial Intelligence since they can be applied to any type…
PerceptNet: A Human Visual System Inspired Neural Network for Estimating Perceptual Distance
Alexander Hepburn, Valero Laparra, Jesús Malo +2
Traditionally, the vision community has devised algorithms to estimate the distance between an original image and images that have been subject to perturbations. Inspiration was us…
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…
FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency
Kacper Sokol, Raul Santos-Rodriguez, Peter Flach
Today, artificial intelligence systems driven by machine learning algorithms can be in a position to take important, and sometimes legally binding, decisions about our everyday liv…
Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback
Taku Yamagata, Raúl Santos-Rodríguez, Ryan McConville +1
Recent advances in both machine learning and Internet-of-Things have attracted attention to automatic Activity Recognition, where users wear a device with sensors and their outputs…
N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding
Ryan McConville, Raul Santos-Rodriguez, Robert J Piechocki +1
Deep clustering has increasingly been demonstrating superiority over conventional shallow clustering algorithms. Deep clustering algorithms usually combine representation learning…
Location Anomalies Detection for Connected and Autonomous Vehicles
Xiaoyang Wang, Ioannis Mavromatis, Andrea Tassi +2
Future Connected and Automated Vehicles (CAV), and more generally ITS, will form a highly interconnected system. Such a paradigm is referred to as the Internet of Vehicles (herein…
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…
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
Bo Kang, Darío García García, Jefrey Lijffijt +2
Dimensionality reduction and manifold learning methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE) are routinely used to map high-dimensional data into a 2-dimensio…
Neural ODEs with stochastic vector field mixtures
Niall Twomey, Michał Kozłowski, Raúl Santos-Rodríguez
It was recently shown that neural ordinary differential equation models cannot solve fundamental and seemingly straightforward tasks even with high-capacity vector field representa…
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…
Informative Data Projections: A Framework and Two Examples
Tijl De Bie, Jefrey Lijffijt, Raul Santos-Rodriguez +1
Methods for Projection Pursuit aim to facilitate the visual exploration of high-dimensional data by identifying interesting low-dimensional projections. A major challenge is the de…