290 citations · 469 across the 35 of their papers we have counts for
8 papers · 1 filter
Multi-lingual agents through multi-headed neural networks
J. D. Thomas, R. Santos-Rodríguez, R. Piechocki +1
This paper considers cooperative Multi-Agent Reinforcement Learning, focusing on emergent communication in settings where multiple pairs of independent learners interact at varying…
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…
On the relation between statistical learning and perceptual distances
Alexander Hepburn, Valero Laparra, Raul Santos-Rodriguez +2
It has been demonstrated many times that the behavior of the human visual system is connected to the statistics of natural images. Since machine learning relies on the statistics o…
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…
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…
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…
Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception
Alexander Hepburn, Raul Santos-Rodriguez
Explaining the decisions of models is becoming pervasive in the image processing domain, whether it is by using post-hoc methods or by creating inherently interpretable models. Whi…