11 citations · 11 across the 1 of their papers we have counts for
5 papers
Second Order PAC-Bayesian Bounds for the Weighted Majority Vote
Andrés R. Masegosa, Stephan S. Lorenzen, Christian Igel +1
We present a novel analysis of the expected risk of weighted majority vote in multiclass classification. The analysis takes correlation of predictions by ensemble members into acco…
Learning under Model Misspecification: Applications to Variational and Ensemble methods
Andres R. Masegosa
Virtually any model we use in machine learning to make predictions does not perfectly represent reality. So, most of the learning happens under model misspecification. In this work…
Probabilistic Models with Deep Neural Networks
Andrés R. Masegosa, Rafael Cabañas, Helge Langseth +2
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to (i) very re…
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy
Javier Cózar, Rafael Cabañas, Antonio Salmerón +1
InferPy is a Python package for probabilistic modeling with deep neural networks. It defines a user-friendly API that trades-off model complexity with ease of use, unlike other lib…
Bayesian Models of Data Streams with Hierarchical Power Priors
Andres Masegosa, Thomas D. Nielsen, Helge Langseth +3
Making inferences from data streams is a pervasive problem in many modern data analysis applications. But it requires to address the problem of continuous model updating and adapt…