Neural Bayesian Anomaly Mitigation: A Robust Loss that Doubles as an Unsupervised Contamination Classifier
S. A. K. Leeney, W. J. Handley, H. T. J. Bevins, E. de Lera Acedo
cs.LG, astro-ph.CO, stat.ML
Submitted: 2026-06-15
Comments: 13 pages, 4 figures
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- JAX-bandflux: differentiable supernovae SALT modelling for cosmological analysis on GPUs
- Bayesian Anomaly Detection for Ia Cosmology: Automating SALT3 Data Curation
- Conditional Neural Bayes Ratio Estimation for Experimental Design Optimisation
- Towards the Identifiability in Noisy Label Learning: A Multinomial Mixture Modelling Approach
Related papers
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
- AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
- Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
- Convergence issues in Relational Concept Analysis based on AOC-posets
- Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
- Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks