13 papers
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…
Kernel Regression with Tensor Trains and Hadamard Overparameterization
Duc Thien Nguyen, Konstantinos Slavakis, Eleftherios Kofidis +1
Kernel regression with tensor trains and Hadamard overparameterization (KReTTaH) is introduced as a training-data-free, interpretable, and nonparametric framework for multi-way dat…
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…
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…
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…
LogosQ: A High-Performance and Type-Safe Quantum Computing Library in Rust
Shiwen An, Jiayi Wang, Konstantinos Slavakis
Developing robust and high performance quantum software is challenging due to the dynamic nature of existing Python-based frameworks, which often suffer from runtime errors and sca…