3 papers
cs.LG2026
Decision-Aligned Evaluation of Uncertainty Quantification
Annika Schneider, Tommy Rochussen, Joshua Stiller +1
Uncertainty estimates in machine learning are typically evaluated using generic metrics such as the negative log-likelihood and expected calibration error, yet good performance on…
stat.ML2026
Amortising Inference and Meta-Learning Priors in Neural Networks
Tommy Rochussen, Vincent Fortuin
One of the core facets of Bayesianism is in the updating of prior beliefs in light of new evidenceso how can we maintain a Bayesian approach if we have no prior belief…
stat.ML2025
Sparse Gaussian Neural Processes
Tommy Rochussen, Vincent Fortuin
Despite significant recent advances in probabilistic meta-learning, it is common for practitioners to avoid using deep learning models due to a comparative lack of interpretability…