no code implementations • 29 Mar 2024 • Yuki Akiyama, Minh Vu, Konstantinos Slavakis
This paper designs novel nonparametric Bellman mappings in reproducing kernel Hilbert spaces (RKHSs) for reinforcement learning (RL).
no code implementations • 6 Feb 2024 • Duc Thien Nguyen, Konstantinos Slavakis
This paper introduces a novel nonparametric framework for data imputation, coined multilinear kernel regression and imputation via the manifold assumption (MultiL-KRIM).
no code implementations • 14 Sep 2023 • Yuki Akiyama, Konstantinos Slavakis
These mappings are defined in reproducing kernel Hilbert spaces (RKHSs), to benefit from the rich approximation properties and inner product of RKHSs, they are shown to belong to the powerful Hilbertian family of (firmly) nonexpansive mappings, regardless of the values of their discount factors, and possess ample degrees of design freedom to even reproduce attributes of the classical Bellman mappings and to pave the way for novel RL designs.
no code implementations • 6 Apr 2023 • Duc Thien Nguyen, Konstantinos Slavakis
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).
no code implementations • 21 Oct 2022 • Yuki Akiyama, Minh Vu, Konstantinos Slavakis
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.
no code implementations • 20 Oct 2022 • Minh Vu, Yuki Akiyama, Konstantinos Slavakis
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 outliers.
no code implementations • 27 Feb 2020 • Gaurav N. Shetty, Konstantinos Slavakis, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying
This paper establishes a kernel-based framework for reconstructing data on manifolds, tailored to fit the dynamic-(d)MRI-data recovery problem.
no code implementations • 18 Feb 2020 • Cong Ye, Konstantinos Slavakis, Pratik V. Patil, Johan Nakuci, Sarah F. Muldoon, John Medaglia
This paper introduces a clustering framework for networks with nodes annotated with time-series data.
no code implementations • 11 Oct 2019 • Konstantinos Slavakis, Sinjini Banerjee
This paper fortifies the recently introduced hierarchical-optimization recursive least squares (HO-RLS) against outliers which contaminate infrequently linear-regression models.
no code implementations • 5 Jun 2019 • Cong Ye, Konstantinos Slavakis, Pratik V. Patil, Sarah F. Muldoon, John Medaglia
Recent advances in neuroscience and in the technology of functional magnetic resonance imaging (fMRI) and electro-encephalography (EEG) have propelled a growing interest in brain-network clustering via time-series analysis.
no code implementations • 27 Dec 2018 • Gaurav N. Shetty, Konstantinos Slavakis, Abhishek Bose, Ukash Nakarmi, Gesualdo Scutari, Leslie Ying
This paper puts forth a novel bi-linear modeling framework for data recovery via manifold-learning and sparse-approximation arguments and considers its application to dynamic magnetic-resonance imaging (dMRI).
no code implementations • 26 Jan 2017 • Konstantinos Slavakis, Shiva Salsabilian, David S. Wack, Sarah F. Muldoon, Henry E. Baidoo-Williams, Jean M. Vettel, Matthew Cieslak, Scott T. Grafton
This paper advocates Riemannian multi-manifold modeling in the context of network-wide non-stationary time-series analysis.
no code implementations • 6 Oct 2015 • Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis
At the heart of SkeVa-SC lies a randomized scheme for approximating the underlying probability density function of the observed data by kernel smoothing arguments.
no code implementations • 29 Jan 2015 • Konstantinos Slavakis, Georgios B. Giannakis
Applications involving dictionary learning, non-negative matrix factorization, subspace clustering, and parallel factor tensor decomposition tasks motivate well algorithms for per-block-convex and non-smooth optimization problems.
no code implementations • 22 Jan 2015 • Panagiotis A. Traganitis, Konstantinos Slavakis, Georgios B. Giannakis
In response to the need for learning tools tuned to big data analytics, the present paper introduces a framework for efficient clustering of huge sets of (possibly high-dimensional) data.
no code implementations • 1 Oct 2014 • Xu Wang, Konstantinos Slavakis, Gilad Lerman
This paper advocates a novel framework for segmenting a dataset in a Riemannian manifold $M$ into clusters lying around low-dimensional submanifolds of $M$.