長期的ダイナミクス学習のためのギブス変分最終層
Gibbs Variational Last Layers for Long-Horizon Dynamics Learning
変分ベイズ最終層(VBLL)をギブス変分推論の観点から再解釈し、多段階予測損失やCRPS、自己回帰ロールアウトを取り入れた学習法を提案。カオス系や実データで長期予測精度と分布適合が向上することを示した。
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著者: Paul Brunzema, Henrik Hose, Sebastian Trimpe
分類: cs.LG, cs.RO
原文アブストラクト
Accurate long-horizon probabilistic predictions of dynamical systems are a prerequisite for model-based decision-making. Variational Bayesian last layers (VBLLs) are an attractive model class for this setting, offering tractable uncertainty at near-deterministic cost, yet they are trained exclusively with single-step likelihood objectives that may be insufficient for reliable multi-step rollouts. We revisit VBLLs through the lens of Gibbs variational inference, which decouples posterior inference from the choice of training loss. Building on this perspective, we introduce a family of multi-step training losses that progressively incorporate long-horizon structure, robustness via continuous ranked probability scores (CRPS), and training on full autoregressive rollouts. Our approach retains the simplicity of VBLLs while directly optimizing for multi-step predictive quality. Across illustrative synthetic environments, a diverse suite of chaotic dynamical systems, and real-world data, we show that multi-step, CRPS-based objectives substantially improve long-horizon accuracy and distributional fit, consistently outperforming single-step likelihood training.