Zobrazeno 1 - 10
of 499
pro vyhledávání: '"Slavakis A"'
This paper extends the recently developed framework of multilinear kernel regression and imputation via manifold learning (MultiL-KRIM) to impute time-varying edge flows in a graph. MultiL-KRIM uses simplicial-complex arguments and Hodge Laplacians t
Externí odkaz:
http://arxiv.org/abs/2409.05135
Autor:
Vu, Minh, Slavakis, Konstantinos
This paper establishes a novel role for Gaussian-mixture models (GMMs) as functional approximators of Q-function losses in reinforcement learning (RL). Unlike the existing RL literature, where GMMs play their typical role as estimates of probability
Externí odkaz:
http://arxiv.org/abs/2409.04374
This paper designs novel nonparametric Bellman mappings in reproducing kernel Hilbert spaces (RKHSs) for reinforcement learning (RL). The proposed mappings benefit from the rich approximating properties of RKHSs, adopt no assumptions on the statistic
Externí odkaz:
http://arxiv.org/abs/2403.20020
This paper introduces a novel nonparametric framework for data imputation, coined multilinear kernel regression and imputation via the manifold assumption (MultiL-KRIM). Motivated by manifold learning, MultiL-KRIM models data features as a point clou
Externí odkaz:
http://arxiv.org/abs/2402.03648
Autor:
Akiyama, Yuki, Slavakis, Konstantinos
This paper aims at the algorithmic/theoretical core of reinforcement learning (RL) by introducing the novel class of proximal Bellman mappings. These mappings are defined in reproducing kernel Hilbert spaces (RKHSs), to benefit from the rich approxim
Externí odkaz:
http://arxiv.org/abs/2309.07548
This paper introduces an efficient multi-linear nonparametric (kernel-based) approximation framework for data regression and imputation, and its application to dynamic magnetic-resonance imaging (dMRI). Data features are assumed to reside in or close
Externí odkaz:
http://arxiv.org/abs/2304.03041
This paper introduces a solution to the problem of selecting dynamically (online) the ``optimal'' p-norm to combat outliers in linear adaptive filtering without any knowledge on the probability density function of the outliers. The proposed online an
Externí odkaz:
http://arxiv.org/abs/2210.11755
This study addresses the problem of selecting dynamically, at each time instance, the ``optimal'' p-norm to combat outliers in linear adaptive filtering without any knowledge on the potentially time-varying probability distribution function of the ou
Externí odkaz:
http://arxiv.org/abs/2210.11317
This paper establishes a kernel-based framework for reconstructing data on manifolds, tailored to fit the dynamic-(d)MRI-data recovery problem. The proposed methodology exploits simple tangent-space geometries of manifolds in reproducing kernel Hilbe
Externí odkaz:
http://arxiv.org/abs/2002.11885
Autor:
Ye, Cong, Slavakis, Konstantinos, Patil, Pratik V., Nakuci, Johan, Muldoon, Sarah F., Medaglia, John
This paper introduces a clustering framework for networks with nodes annotated with time-series data. The framework addresses all types of network-clustering problems: State clustering, node clustering within states (a.k.a. topology identification or
Externí odkaz:
http://arxiv.org/abs/2002.09943