156 citations · 950 across the 61 of their papers we have counts for
8 papers · 1 filter
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…
Upper and Lower Bounds on the Performance of Kernel PCA
Maxime Haddouche, Benjamin Guedj, John Shawe-Taylor
Principal Component Analysis (PCA) is a popular method for dimension reduction and has attracted an unfailing interest for decades. More recently, kernel PCA (KPCA) has emerged as…
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…
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…
PAC-Bayes unleashed: generalisation bounds with unbounded losses
Maxime Haddouche, Benjamin Guedj, Omar Rivasplata +1
We present new PAC-Bayesian generalisation bounds for learning problems with unbounded loss functions. This extends the relevance and applicability of the PAC-Bayes learning framew…