189 citations · 202 across the 18 of their papers we have counts for
4 papers · 1 filter
Incremental Recommendation via Causal Models
Athanasios Vlontzos, David Gustafsson, Michael O'Riordan +1
Recommendation impressions are a finite resource, hence delivering a recommendation to a user who would discover the content organically yields no incremental value and displaces o…
Unsupervised Identification and Removal of Spurious Correlations During Fine-Tuning
Ciarán M. Gilligan-Lee, Joseph Egan, Yuchen Zhu +1
Fine-tuning a pretrained language model on a curated dataset can produce spurious correlations between the fine-tuning task and unintended latent factors---such as misaligned perso…
Causal Representation Learning for Generalisable Recommendation
Yorgos Felekis, Michael O'Riordan, Oriol Corcoll +1
Predictive models trained on observational data often fail to generalise when deployed, especially when the training data is a product of the system being optimised. Recommender sy…
The resource theory of causal influence and knowledge of causal influence
Marina Maciel Ansanelli, Beata Zjawin, David Schmid +5
Understanding and quantifying causal relationships between variables is essential for reasoning about the physical world. In this work, we develop a resource-theoretic framework to…