arXiv:2608. 12879v1 Announce Type: new Abstract: Fractional partial differential equations describe nonlocal dynamics, but discovering them from noisy data is difficult because fractional differentiation amplifies high-frequency measurement noise and the derivative orders are unknown.
By Pongpisit Thanasutives, Yoshinobu Kawahara
arXiv:2609.36314v1 Announce Type: new
Abstract: State Space Models (SSMs) compress sequence history into a bounded recurrent state, making the resulting memory law a central architectural choice for...
By Ivan Kobyzev, Abbas Ghaddar, Ali Nasiri-Sarvi, Lifeng Shang, Yufei Cui
arXiv:2606. 05967v1 Announce Type: cross Abstract: In this paper, we study the finite-time behavior of the TD(0) temporal-difference method with linear function approximation (LFA).
By Ziad Kobeissi (L2S), \'Elo\"ise Berthier (U2IS)
arXiv:2504. 17503v2 Announce Type: replace Abstract: We study how the degree of nonlinearity in the input data affects the optimal design of reservoir computers, focusing on how closely the model's nonlinearity should align with that of the data.
By Davide Prosperino, Haochun Ma, Christoph R\"ath
The paper studies least squares parameter estimation for discrete‑time, unstable, closed‑loop nonlinear stochastic systems with linearly parametrised uncertainty and additive i.i.d. process noise. By perturbing the control policy with exploratory input and assuming a sub‑exponential input‑to‑state growth property, the authors derive non‑asymptotic bounds on the estimation error whenever the state trajectory remains in an informative region of the state space. When the entire state space is informative, the bounds hold with high probability for all time steps, and the authors illustrate the applicability of their results with examples that extend beyond existing work.
By Seth Siriya, Jingge Zhu, Dragan Ne\v{s}i\'c, Ye Pu
In this paper, we study the finite-time behavior of the TD(0) temporal-difference method with linear function approximation (LFA). We consider on-policy independent and identically distributed (i.