no code implementations • 2 Jan 2024 • Ioar Casado, Luis A. Ortega, Andrés R. Masegosa, Aritz Pérez
This result can be understood as a PAC-Bayesian version of the Cram\'er-Chernoff bound.
no code implementations • 2 Oct 2023 • Yijie Zhang, Yi-Shan Wu, Luis A. Ortega, Andrés R. Masegosa
The cold posterior effect (CPE) (Wenzel et al., 2020) in Bayesian deep learning shows that, for posteriors with a temperature $T<1$, the resulting posterior predictive could have better performances than the Bayesian posterior ($T=1$).
no code implementations • 19 Jun 2023 • Andrés R. Masegosa, Luis A. Ortega
This paper introduces a distribution-dependent PAC-Chernoff bound that exhibits perfect tightness for interpolators, even within over-parameterized model classes.
1 code implementation • 26 Oct 2021 • Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa
In this work, we combine and expand previously published results in a theoretically sound framework that describes the relationship between diversity and ensemble performance for a wide range of ensemble methods.
1 code implementation • NeurIPS 2021 • Yi-Shan Wu, Andrés R. Masegosa, Stephan S. Lorenzen, Christian Igel, Yevgeny Seldin
The bound is based on a novel parametric form of the Chebyshev- Cantelli inequality (a. k. a.
1 code implementation • NeurIPS 2020 • Andrés R. Masegosa, Stephan S. Lorenzen, Christian Igel, Yevgeny Seldin
We present a novel analysis of the expected risk of weighted majority vote in multiclass classification.
no code implementations • 29 Aug 2019 • Javier Cózar, Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa
InferPy is a Python package for probabilistic modeling with deep neural networks.
2 code implementations • 9 Aug 2019 • Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling.
1 code implementation • 4 Apr 2017 • Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen
The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data.