arXiv:2603.26671v1 Announce Type: new
Abstract: As neural networks are increasingly deployed in dynamic environments, they face the challenge of catastrophic forgetting, the tendency to overwrite previously learned knowledge when adapting to new tasks…
arXiv:2603.27579v1 Announce Type: new
Abstract: Cartoon-texture image decomposition is a fundamental yet challenging problem in image processing. A significant hurdle in achieving accurate decomposition is the pervasive presence of noise in the observ…
arXiv:2603.26542v2 Announce Type: replace-cross
Abstract: Buffer zones are essential in production systems to decouple sequential processes. In dense floor storage environments, such as space-constrained brownfield facilities, manual operation is incr…
arXiv:2603.27066v1 Announce Type: new
Abstract: Matching plays an important role in the logical allocation of resources across a wide range of industries. The benefits of matching have been increasingly recognized in manufacturing industries. In parti…
arXiv:2603.27370v1 Announce Type: cross
Abstract: This paper revisits and extends the 2013 development by Rockafellar and Uryasev of the Risk Quadrangle (RQ) as a unified scheme for integrating risk management, optimization, and statistical estimation…
arXiv:2603.28448v1 Announce Type: cross
Abstract: We propose Yau’s Affine Normal Descent (YAND), a geometric framework for smooth unconstrained optimization in which search directions are defined by the equi-affine normal of level-set hypersurfaces. T…
arXiv:2603.26542v2 Announce Type: replace
Abstract: Buffer zones are essential in production systems to decouple sequential processes. In dense floor storage environments, such as space-constrained brownfield facilities, manual operation is increasing…
arXiv:2603.27159v1 Announce Type: new
Abstract: In this paper, we study the problem of learning Kalman filtering with unknown system model in partially observed linear dynamical systems. We propose a unified algorithmic framework based on online optim…
arXiv:2506.23836v2 Announce Type: replace-cross
Abstract: We consider centralized distributed optimization in the classical federated learning setup, where $n$ workers jointly find an $\varepsilon$-stationary point of an $L$-smooth, $d$-dimensional no…
arXiv:2402.05689v4 Announce Type: replace
Abstract: We consider the infinite-horizon, average-reward restless bandit problem in discrete time. We propose a new class of policies that are designed to drive a progressively larger subset of arms toward t…