7 citations · 12 across the 8 of their papers we have counts for
9 papers · 1 filter
A Note on the Convergence of Denoising Diffusion Probabilistic Models
Sokhna Diarra Mbacke, Omar Rivasplata
Diffusion models are one of the most important families of deep generative models. In this note, we derive a quantitative upper bound on the Wasserstein distance between the data-g…
Semi-supervised Batch Learning From Logged Data
Gholamali Aminian, Armin Behnamnia, Roberto Vega +5
Off-policy learning methods are intended to learn a policy from logged data, which includes context, action, and feedback (cost or reward) for each sample point. In this work, we b…
Progress in Self-Certified Neural Networks
Maria Perez-Ortiz, Omar Rivasplata, Emilio Parrado-Hernandez +2
A learning method is self-certified if it uses all available data to simultaneously learn a predictor and certify its quality with a tight statistical certificate that is valid on…
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…
On the Role of Optimization in Double Descent: A Least Squares Study
Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata +2
Empirically it has been observed that the performance of deep neural networks steadily improves as we increase model size, contradicting the classical view on overfitting and gener…
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…