3 papers
stat.CO2026
Understanding uncertainty in Bayesian cluster analysis
Cecilia Balocchi, Sara Wade
The Bayesian approach to clustering is often appreciated for its ability to provide uncertainty in the partition structure. However, summarizing the posterior distribution over the…
cs.LG2025
Understanding the Trade-offs in Accuracy and Uncertainty Quantification: Architecture and Inference Choices in Bayesian Neural Networks
Alisa Sheinkman, Sara Wade
As modern neural networks get more complex, specifying a model with high predictive performance and sound uncertainty quantification becomes a more challenging task. Despite some p…
stat.ML2025
Variational Bayesian Bow tie Neural Networks with Shrinkage
Alisa Sheinkman, Sara Wade
Despite the dominant role of deep models in machine learning, limitations persist, including overconfident predictions, susceptibility to adversarial attacks, and underestimation o…