Marı́a Pérez-Ortiz

Publications

Leveraging Semantic Knowledge Graphs in Educational Recommenders to Address the Cold-Start Problem (2023)
TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback (2023)
Comparing the carbon costs and benefits of low-resource solar nowcasting (2022)
HHAI2022: Augmenting Human Intellect: Proceedings of the First International Conference on Hybrid Human-Artificial Intelligence (2022)
Network topological determinants of pathogen spread (2022)
Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources (2022)
Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution (2021)
Learning PAC-Bayes Priors for Probabilistic Neural Networks (2021)
Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems (2021)
Tighter Risk Certificates for Neural Networks (2021)
X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI (2021)
A PAC-Bayesian perspective on structured prediction with implicit loss embeddings (2020)
Contrast Sensitivity Functions for HDR Displays (2020)
Deep learning for monthly Arctic sea ice concentration prediction (2020)
Predicting Engagement in Video Lectures (2020)
Spatio-chromatic contrast sensitivity under mesopic and photopic light levels (2020)
SUM'20: State-based User Modelling (2020)
Towards self-certified learning: Probabilistic neural networks trained by PAC-Bayes with Backprop (2020)
What's in it for me? Augmenting Recommended Learning Resources with Navigable Annotations (2020)
On the use of evolutionary time series analysis for segmenting paleoclimate data (2019)
Orca: A matlab/octave toolbox for ordinal regression (2019)
Visibility Metric for Visually Lossless Image Compression (2019)
Binary ranking for ordinal class imbalance (2018)
Partial order label decomposition approaches for melanoma diagnosis (2018)
Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem (2017)
A review of classification problems and algorithms in renewable energy applications (2016)
A study on multi-scale kernel optimisation via centered kernel-target alignment (2016)
Classification of melanoma presence and thickness based on computational image analysis (2016)
Fisher score-based feature selection for ordinal classification: A social survey on subjective well-being (2016)
Machine learning paradigms for weed mapping via unmanned aerial vehicles (2016)
On the use of nominal and ordinal classifiers for the discrimination of states of development in fish oocytes (2016)
Ordinal evolutionary artificial neural networks for solving an imbalanced liver transplantation problem (2016)
Representing ordinal input variables in the context of ordinal classification (2016)
Selecting patterns and features for between-and within-crop-row weed mapping using UAV-imagery (2016)
Semi-supervised learning for ordinal Kernel Discriminant Analysis (2016)
Tackling the ordinal and imbalance nature of a melanoma image classification problem (2016)
An experimental comparison for the identification of weeds in sunflower crops via unmanned aerial vehicles and object-based analysis (2015)
Kernelising the Proportional Odds Model through kernel learning techniques (2015)
Oversampling the Minority Class in the Feature Space (2015)
Classification of EU countries’ progress towards sustainable development based on ordinal regression techniques (2014)
An n-spheres based synthetic data generator for supervised classification (2013)
Borderline kernel based over-sampling (2013)
Kernelizing the proportional odds model through the empirical kernel mapping (2013)
Multi-scale Support Vector Machine Optimization by Kernel Target-Alignment. (2013)
Projection-Based Ensemble Learning for Ordinal Regression (2013)
An ensemble approach for ordinal threshold models applied to liver transplantation (2012)