156 citations · 950 across the 61 of their papers we have counts for
62 papers
Human-AI Coevolution
Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina +14
Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artif…
Exploration via Epistemic Value Estimation
Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
How to efficiently explore in reinforcement learning is an open problem. Many exploration algorithms employ the epistemic uncertainty of their own value predictions -- for instance…
Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments
Sahan Bulathwela, Meghana Verma, Maria Perez-Ortiz +2
This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces i) VLE, a novel dataset t…
TransductGAN: a Transductive Adversarial Model for Novelty Detection
Najiba Toron, Janaina Mourao-Miranda, John Shawe-Taylor
Novelty detection, a widely studied problem in machine learning, is the problem of detecting a novel class of data that has not been previously observed. A common setting for novel…
Chaining Value Functions for Off-Policy Learning
Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
To accumulate knowledge and improve its policy of behaviour, a reinforcement learning agent can learn `off-policy' about policies that differ from the policy used to generate its e…
Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound
Reuben Adams, John Shawe-Taylor, Benjamin Guedj
Current PAC-Bayes generalisation bounds are restricted to scalar metrics of performance, such as the loss or error rate. However, one ideally wants more information-rich certificat…