Search Results for author:
Found 4 papers, 1 papers with code
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering
This paper designs novel nonparametric Bellman mappings in reproducing kernel Hilbert spaces (RKHSs) for reinforcement learning (RL).
online and lightweight kernel-based approximated policy iteration for dynamic p-norm linear adaptive filtering
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.
Dynamic selection of p-norm in linear adaptive filtering via online kernel-based reinforcement learning
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.
Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness
In this paper, we propose a novel Heterogeneous Gaussian Mechanism (HGM) to preserve differential privacy in deep neural networks, with provable robustness against adversarial examples.
