6 papers
Universal Adaptive Environment Discovery
Madi Matymov, Ba-Hien Tran, Maurizio Filippone
An open problem in Machine Learning is how to avoid models to exploit spurious correlations in the data; a famous example is the background-label shortcut in the Waterbirds dataset…
TENDE: Transfer Entropy Neural Diffusion Estimation
Simon Pedro Galeano Munoz, Mustapha Bounoua, Giulio Franzese +2
Transfer entropy measures directed information flow in time series, and it has become a fundamental quantity in applications spanning neuroscience, finance, and complex systems ana…
Bridging GANs and Bayesian Neural Networks via Partial Stochasticity
Maurizio Filippone, Marius P. Linhard
Generative Adversarial Networks (GANs) are popular and successful generative models. Despite their success, optimization is notoriously challenging. In this work, we explain the su…
Emergent Granger Causality in Neural Networks: Can Prediction Alone Reveal Structure?
Malik Shahid Sultan, Hernando Ombao, Maurizio Filippone
Granger Causality (GC) offers an elegant statistical framework to study the association between multivariate time series data. Vector autoregressive models (VAR) are simple and eas…
Scaling Laws for Uncertainty in Deep Learning
Mattia Rosso, Simone Rossi, Giulio Franzese +2
Deep learning has recently revealed the existence of scaling laws, demonstrating that model performance follows predictable trends based on dataset and model sizes. Inspired by the…
Optimizing Data Augmentation through Bayesian Model Selection
Madi Matymov, Ba-Hien Tran, Michael Kampffmeyer +2
Data Augmentation (DA) has become an essential tool to improve robustness and generalization of modern machine learning. However, when deciding on DA strategies it is critical to c…