1 citations · 2 across the 5 of their papers we have counts for
5 papers
Argumentation for Explainable and Globally Contestable Decision Support with LLMs
Adam Dejl, Matthew Williams, Francesca Toni
Large language models (LLMs) exhibit strong general capabilities, but their deployment in high-stakes domains is hindered by their opacity and unpredictability. Recent work has tak…
Clustered Federated Learning via Embedding Distributions
Dekai Zhang, Matthew Williams, Francesca Toni
Federated learning (FL) is a widely used framework for machine learning in distributed data environments where clients hold data that cannot be easily centralised, such as for data…
Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)
Adam Gould, Guilherme Paulino-Passos, Seema Dadhania +2
In the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences…
Targeted Activation Penalties Help CNNs Ignore Spurious Signals
Dekai Zhang, Matthew Williams, Francesca Toni
Neural networks (NNs) can learn to rely on spurious signals in the training data, leading to poor generalisation. Recent methods tackle this problem by training NNs with additional…
Hidden Conflicts in Neural Networks and Their Implications for Explainability
Adam Dejl, Dekai Zhang, Hamed Ayoobi +2
Artificial Neural Networks (ANNs) often represent conflicts between features, arising naturally during training as the network learns to integrate diverse and potentially disagreei…