6 papers
Flow-Transformed Implicit Processes for Function-Space Variational Inference
Luis A. Ortega, Andrés R. Masegosa, Thomas D. Nielsen
Implicit-process priors define distributions over functions through flexible generative mechanisms, making them attractive for Bayesian function-space modelling. However, performin…
Deep Actor-Critics with Tight Risk Certificates
Bahareh Tasdighi, Manuel Haussmann, Yi-Shan Wu +2
Deep actor-critic algorithms have reached a level where they influence everyday life. They are a driving force behind continual improvement of large language models through user fe…
UncertainGen: Uncertainty-Aware Representations of DNA Sequences for Metagenomic Binning
Abdulkadir Celikkanat, Andres R. Masegosa, Mads Albertsen +1
Metagenomic binning aims to cluster DNA fragments from mixed microbial samples into their respective genomes, a critical step for downstream analyses of microbial communities. Exis…
PAC-Chernoff Bounds: Understanding Generalization in the Interpolation Regime
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. This bound, which…
Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning
Abdulkadir Celikkanat, Andres R. Masegosa, Thomas D. Nielsen
Obtaining effective representations of DNA sequences is crucial for genome analysis. Metagenomic binning, for instance, relies on genome representations to cluster complex mixtures…
PAC-Bayes-Chernoff bounds for unbounded losses
Ioar Casado, Luis A. Ortega, Aritz Pérez +1
We introduce a new PAC-Bayes oracle bound for unbounded losses that extends Cramér-Chernoff bounds to the PAC-Bayesian setting. The proof technique relies on controlling the tails…