1 citations · 1 across the 2 of their papers we have counts for
3 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…