48 citations · 198 across the 61 of their papers we have counts for
11 papers · 1 filter
Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes
Katarzyna Kobalczyk, Claudio Fanconi, Hao Sun +1
As large language models (LLMs) become increasingly embedded in everyday applications, ensuring their alignment with the diverse preferences of individual users has become a critic…
Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments
Paulius Rauba, Nabeel Seedat, Krzysztof Kacprzyk +1
Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process (DGP). Existing approaches…
On the consistent reasoning paradox of intelligence and optimal trust in AI: The power of 'I don't know'
Alexander Bastounis, Paolo Campodonico, Mihaela van der Schaar +2
We introduce the Consistent Reasoning Paradox (CRP). Consistent reasoning, which lies at the core of human intelligence, is the ability to handle tasks that are equivalent, yet des…
Automated Ensemble Multimodal Machine Learning for Healthcare
Fergus Imrie, Stefan Denner, Lucas S. Brunschwig +2
The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approac…
LaTable: Towards Large Tabular Models
Boris van Breugel, Jonathan Crabbé, Rob Davis +1
Tabular data is one of the most ubiquitous modalities, yet the literature on tabular generative foundation models is lagging far behind its text and vision counterparts. Creating s…
You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling
Nabeel Seedat, Nicolas Huynh, Fergus Imrie +1
Pseudo-labeling is a popular semi-supervised learning technique to leverage unlabeled data when labeled samples are scarce. The generation and selection of pseudo-labels heavily re…