2 citations · 4 across the 16 of their papers we have counts for
13 papers · 1 filter
Online Gradient Computation for Warping Gaussian Process Transformations
Emilio Ruiz-Moreno, Konstantinos Slavakis, Baltasar Beferull-Lozano
Warped Gaussian processes (GPs) handle non-Gaussian observations by mapping them into a latent standard GP via a parametric transformation called warping. Existing streaming varian…
Sparse Gaussian-Mixture-Model Q-Functions via Hadamard Overparametrization for Online Reinforcement Learning
Minh Vu, Konstantinos Slavakis
This paper develops an online, off-policy policy-iteration framework for reinforcement learning (RL), based on sparse Gaussian-mixture-model Q-functions (S-GMM-QFs). The framework…
Non-Convex Sparse Reinforcement Learning via Non-Monotone Inclusions
Kyohei Suzuki, Konstantinos Slavakis
This work delivers two key contributions: one to efficient feature selection in reinforcement learning (RL), the other to the theory of non-monotone inclusions. On the RL side, the…
External Division of Two Bregman Proximity Operators for Poisson Inverse Problems
Kazuki Haishima, Kyohei Suzuki, Konstantinos Slavakis
This paper presents a novel method for recovering sparse vectors from linear models corrupted by Poisson noise. The contribution is twofold. First, an operator defined via the exte…
Gaussian-Mixture-Model Q-Functions for Policy Iteration in Reinforcement Learning
Minh Vu, Konstantinos Slavakis
Unlike their conventional use as estimators of probability density functions in reinforcement learning (RL), this paper introduces a novel function-approximation role for Gaussian…
Kernel Regression of Multi-Way Data via Tensor Trains with Hadamard Overparametrization: The Dynamic Graph Flow Case
Duc Thien Nguyen, Konstantinos Slavakis, Eleftherios Kofidis +1
A regression-based framework for interpretable multi-way data imputation, termed Kernel Regression via Tensor Trains with Hadamard overparametrization (KReTTaH), is introduced. KRe…
Nonconvex Regularization for Feature Selection in Reinforcement Learning
Kyohei Suzuki, Konstantinos Slavakis
This work proposes an efficient batch algorithm for feature selection in reinforcement learning (RL) with theoretical convergence guarantees. To mitigate the estimation bias inhere…
Online reinforcement learning via sparse Gaussian mixture model Q-functions
Minh Vu, Konstantinos Slavakis
This paper introduces a structured and interpretable online policy-iteration framework for reinforcement learning (RL), built around the novel class of sparse Gaussian mixture mode…
Robust Invariant Representation Learning by Distribution Extrapolation
Kotaro Yoshida, Konstantinos Slavakis
Invariant risk minimization (IRM) aims to enable out-of-distribution (OOD) generalization in deep learning by learning invariant representations. As IRM poses an inherently challen…
Nonparametric Bellman Mappings for Value Iteration in Distributed Reinforcement Learning
Yuki Akiyama, Konstantinos Slavakis
This paper introduces novel Bellman mappings (B-Maps) for value iteration (VI) in distributed reinforcement learning (DRL), where agents are deployed over an undirected, connected…
Model-Free Adversarial Purification via Coarse-To-Fine Tensor Network Representation
Guang Lin, Duc Thien Nguyen, Zerui Tao +3
Deep neural networks are known to be vulnerable to well-designed adversarial attacks. Although numerous defense strategies have been proposed, many are tailored to the specific att…
Gaussian-Mixture-Model Q-Functions for Reinforcement Learning by Riemannian Optimization
Minh Vu, Konstantinos Slavakis
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 liter…
Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learning
Duc Thien Nguyen, Konstantinos Slavakis, Dimitris Pados
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…