1 citations · 3 across the 10 of their papers we have counts for
13 papers
Predictive-Context LOO: Bayesian Model Comparison with Pretrained Predictive Learners
Hans Montcho, Maurizio Filippone
Leave-one-out (LOO) cross-validation, a standard tool for Bayesian model assessment, evaluates each observation using a LOO predictive distribution derived from the model itself. I…
Sparsifying Stochasticity, Not Capacity: Partial Stochasticity via Deep Weight Factorization of Prior Scales
Marius P Linhard, Maurizio Filippone
Bayesian neural networks need not be fully stochastic to be universal conditional density approximators, but it remains open which parameters should be stochastic. We learn this sp…
DIPHINE: Diffusion-based -ID Neural Estimator
Simon Pedro Galeano Munoz, Mustapha Bounoua, Giulio Franzese +2
Uncovering the true informational architecture of real-world complex systems requires disentangling how their components uniquely store, redundantly share, and synergistically inte…
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…
Robust Classification by Coupling Data Mollification with Label Smoothing
Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer +1
Introducing training-time augmentations is a key technique to enhance generalization and prepare deep neural networks against test-time corruptions. Inspired by the success of gene…
Permutation-based Inference for Variational Learning of Directed Acyclic Graphs
Edwin V. Bonilla, Pantelis Elinas, He Zhao +3
Estimating the structure of Bayesian networks as directed acyclic graphs (DAGs) from observational data is a fundamental challenge, particularly in causal discovery. Bayesian appro…
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla +22
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language dat…
Spatial Bayesian Neural Networks
Andrew Zammit-Mangion, Michael D. Kaminski, Ba-Hien Tran +2
interpretable, and well understood models that are routinely employed even though, as is revealed through prior and posterior predictive checks, these can poorly characterise the s…