34 citations · 87 across the 9 of their papers we have counts for
9 papers
A Toolbox for Modelling Engagement with Educational Videos
Yuxiang Qiu, Karim Djemili, Denis Elezi +5
With the advancement and utility of Artificial Intelligence (AI), personalising education to a global population could be a cornerstone of new educational systems in the future. Th…
TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback
Yuxiang Qiu, Karim Djemili, Denis Elezi +3
This work describes the TrueLearn Python library, which contains a family of online learning Bayesian models for building educational (or more generally, informational) recommendat…
Comparing the carbon costs and benefits of low-resource solar nowcasting
Ben Dixon, María Pérez-Ortiz, Jacob Bieker
Solar PV yield nowcasting is used to help anticipate peaks and troughs in demand to support grid integration. This paper compares multiple low-resource approaches to nowcasting sol…
Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems
Sahan Bulathwela, María Pérez-Ortiz, Emine Yilmaz +1
In informational recommenders, many challenges arise from the need to handle the semantic and hierarchical structure between knowledge areas. This work aims to advance towards buil…
Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution
Sahan Bulathwela, María Pérez-Ortiz, Catherine Holloway +1
Artificial Intelligence (AI) in Education has been said to have the potential for building more personalised curricula, as well as democratising education worldwide and creating a…
A PAC-Bayesian Perspective on Structured Prediction with Implicit Loss Embeddings
Théophile Cantelobre, Benjamin Guedj, María Pérez-Ortiz +1
Many practical machine learning tasks can be framed as Structured prediction problems, where several output variables are predicted and considered interdependent. Recent theoretica…
Tighter risk certificates for neural networks
María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor +1
This paper presents an empirical study regarding training probabilistic neural networks using training objectives derived from PAC-Bayes bounds. In the context of probabilistic neu…
Predicting Engagement in Video Lectures
Sahan Bulathwela, María Pérez-Ortiz, Aldo Lipani +2
The explosion of Open Educational Resources (OERs) in the recent years creates the demand for scalable, automatic approaches to process and evaluate OERs, with the end goal of iden…
Machine learning techniques to select Be star candidates. An application in the OGLE-IV Gaia south ecliptic pole field
M. F. Pérez-Ortiz, A. García-Varela, A. J. Quiroz +2
Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised…