Gibbs Variational Last Layers for Long-Horizon Dynamics Learning
Overview
This research paper proposes a method using Gibbs variational inference to improve long-horizon probabilistic predictions in dynamical systems. The authors introduce multi-step training losses that incorporate long-horizon structure and robustness via continuous ranked probability scores (CRPS) for Variational Bayesian last layers (VBLLs). Evaluated across synthetic environments, chaotic dynamical systems, and real-world data, the approach shows substantial improvements in long-horizon accuracy and distributional fit compared to traditional single-step likelihood training.
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- arXiv Robotics — research abstractsSignalGibbs Variational Last Layers for Long-Horizon Dynamics Learning
This research paper proposes a method using Gibbs variational inference to improve long-horizon probabilistic predictions in dynamical systems. The authors introduce multi-step training losses that incorporate long-horizon structure and robustness via continuous ranked probability scores (CRPS) for Variational Bayesian last layers (VBLLs). Evaluated across synthetic environments, chaotic dynamical systems, and real-world data, the approach shows substantial improvements in long-horizon accuracy and distributional fit compared to traditional single-step likelihood training.
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