arXiv:2610. 33412v1 Announce Type: new Abstract: In dynamical systems, feedback can improve performance by allowing decisions to depend on past observations, rather than being designed a priori.
By Leo Maynard-Zhang, Sunmook Choi, Yahya Sattar, Yassir Jedra, Sarah Dean, Maryam Fazel
arXiv:2511.08097v2 Announce Type: replace-cross
Abstract: We consider a general infinite horizon Heterogeneous Restless multi-armed Bandit (RMAB). Heterogeneity is a fundamental problem for many real...
By Dheeraj Narasimha, Nicolas Gast
The paper studies the trade‑off between maximizing rewards and accurately estimating current treatment effects in nonstationary multi‑armed bandits. For $K$‑armed bandits with H"older‑smooth mean rewards, the authors derive minimax bounds on expected dynamic regret under a high‑probability simultaneous coverage requirement for all current effects. They propose the TRACE algorithm, which achieves a regret–precision trade‑off that separates reward‑learning regret from the unavoidable cost of refreshing current‑effect estimates, and extend the method with an empirical‑Bernstein e‑process to adapt exploration while preserving worst‑case guarantees.
By Sharan Sahu, Martin T. Wells
arXiv:2607. 17432v1 Announce Type: new Abstract: Adaptive agents do not always regulate under the same timing conditions.
By Veronique Ziegler
arXiv:2603. 14762v3 Announce Type: replace-cross Abstract: We study supervisory switching control for partially-observed linear dynamical systems.
By Haoyuan Sun, Ali Jadbabaie
arXiv:2608. 13651v1 Announce Type: cross Abstract: We solve exactly a fundamental problem of adaptive control against adversarial disturbances: regulate the scalar system $x_{t+1} = ax_t + u_t + w_t$, $x_0=0$, $\|w\|_\infty \le 1$, where the constant pole $a \in [-\Delta, \Delta]$ is unknown in sign and magnitude and $\Delta$ is arbitrarily large.
By Dimitar Ho