Full-Covariance Smoothing of Bayesian Neural Networks for Online Adaptation

arXiv:2609.27244 · cs.LG, cs.SY, eess.SY · Submitted 2026-09-23 · Read on arXiv

cs.LG, cs.SY, eess.SY

Submitted: 2026-09-23

Updated: 2026-09-23

Comments: Accepted to CDC 2026

Code: https://github.com/omwright/bnn-smoother

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 10× in this paper · explained in 1 episode
• Accuracy — 5× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Covariance Propagation — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Online Adaptation — 4× in this paper · explained in 3 episodes
• Aleatoric Uncertainty — 3× in this paper · explained in 4 episodes
• AdamW Optimizer — 2× in this paper · explained in 1 episode
• Bayesian Inference — 2× in this paper · explained in 5 episodes
• Epistemic Uncertainty — 2× in this paper · explained in 9 episodes
• International Conference on Learning Representations — 2× in this paper
• International Conference on Machine Learning — 2× in this paper
• Activation Function — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Gymnasium: A Standard Interface for Reinforcement Learning Environments

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