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Found 7 papers, 1 papers with code
Safe Reinforcement Learning for Constrained Markov Decision Processes with Stochastic Stopping Time
In this paper, we present an online reinforcement learning algorithm for constrained Markov decision processes with a safety constraint.
PAC-Bayes Generalisation Bounds for Dynamical Systems Including Stable RNNs
In this paper, we derive a PAC-Bayes bound on the generalisation gap, in a supervised time-series setting for a special class of discrete-time non-linear dynamical systems.
Online Model-free Safety Verification for Markov Decision Processes Without Safety Violation
We then use an off-policy temporal difference learning method with importance sampling to learn the safety function corresponding to the given policy.
PAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models
In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short).
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
Privacy has become a major concern in machine learning.
Explicit construction of the minimum error variance estimator for stochastic LTI state-space systems
In this short article, we showcase the derivation of the optimal (minimum error variance) estimator, when one part of the stochastic LTI system output is not measured but is able to be predicted from the measured system outputs.
PAC-Bayesian theory for stochastic LTI systems
In this paper we derive a PAC-Bayesian error bound for autonomous stochastic LTI state-space models.
