activity
20172021
most citedA practical guide and software for analysing pairwise comparison experiments

49 citations · 79 across the 9 of their papers we have counts for

collaborators

10 papers

cs.LG20217 cited

Learning PAC-Bayes Priors for Probabilistic Neural Networks

Maria Perez-Ortiz, Omar Rivasplata, Benjamin Guedj +5

Recent works have investigated deep learning models trained by optimising PAC-Bayes bounds, with priors that are learnt on subsets of the data. This combination has been shown to l…

cs.IR20214 cited

PEEK: A Large Dataset of Learner Engagement with Educational Videos

Sahan Bulathwela, Maria Perez-Ortiz, Erik Novak +2

Educational recommenders have received much less attention in comparison to e-commerce and entertainment-related recommenders, even though efficient intelligent tutors have great p…

eess.IV2020

Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality

Aliaksei Mikhailiuk, Maria Perez-Ortiz, Dingcheng Yue +2

Increasing popularity of high-dynamic-range (HDR) image and video content brings the need for metrics that could predict the severity of image impairments as seen on displays of di…

cs.CY20209 cited

VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement

Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz +1

With the emergence of e-learning and personalised education, the production and distribution of digital educational resources have boomed. Video lectures have now become one of the…

cs.LG20201 cited

Active Sampling for Pairwise Comparisons via Approximate Message Passing and Information Gain Maximization

Aliaksei Mikhailiuk, Clifford Wilmot, Maria Perez-Ortiz +2

Pairwise comparison data arise in many domains with subjective assessment experiments, for example in image and video quality assessment. In these experiments observers are asked t…

cs.IR20198 cited

Towards an Integrative Educational Recommender for Lifelong Learners

Sahan Bulathwela, Maria Perez-Ortiz, Emine Yilmaz +1

One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities betwee…