The paper introduces AURA, a meta‑learning framework that learns a low‑dimensional latent state‑space model for the evolution of optimal model parameters under distribution shift. Online adaptation is performed via extended Kalman filtering in this latent space, followed by reconstruction of full model parameters through a learned lifting map, enabling efficient single‑step updates. Experiments on neural wireless receivers and non‑stationary image classification show that AURA improves adaptation speed, accuracy, and computational efficiency compared to existing online learning and Bayesian filtering baselines.
By Guy Gerson, Tomer Raviv, Nir Shlezinger, Tirza Routtenberg, Osvaldo Simeone
The paper proposes treating a neural network’s layers as time steps in a state‑space model, converting Bayesian training into a smoothing problem. By propagating Gaussian moments forward and applying a Rauch–Tung–Striebel backward pass, weight posteriors are updated in closed form without gradient iterations or replay. The authors extend prior work by introducing a cross‑covariance identity that allows full‑covariance propagation through nonlinear activations, enabling more accurate online adaptation in non‑stationary classification, dynamics learning, and vision‑language‑action policy adaptation.
By Oren Wright, Haoming Jing, Qiaoan Shen, Koichiro Niinuma, Yorie Nakahira, Jos\'e M. F. Moura
The paper studies a two‑stage learning framework that first trains an offline model using approximate nonlinear‑least‑squares estimation and then adapts it online with a meta‑LMS algorithm to handle parameter drift in nonlinear stochastic dynamical systems. It provides an upper bound on the offline generalization error that accounts for strong data correlation and distribution shift via Kullback‑Leibler divergence, and it demonstrates that the combined offline‑online approach outperforms methods that rely solely on offline or online learning. Both theoretical analysis and empirical experiments support the claimed performance gains.
By Haizheng Li, Lei Guo
arXiv:2602. 23050v2 Announce Type: replace Abstract: Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data.
By Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt
arXiv:2609.36712v1 Announce Type: cross
Abstract: Accurately learning nonlinear dynamics from a finite-duration experiment requires the efficient collection of informative data. We address this chall...
By Juncal Arbelaiz, Anushri Arora, Jonathan W. Pillow
arXiv:2607. 06079v1 Announce Type: new Abstract: Intelligent systems should not only solve tasks but also adapt under real-world constraints.
By Taiki Yamada, Kantaro Fujiwara