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Found 9 papers, 0 papers with code
Tight Finite Time Bounds of Two-Time-Scale Linear Stochastic Approximation with Markovian Noise
SA appears in many areas such as optimization and Reinforcement Learning (RL).
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling
To obtain these results, we show that federated TD and Q-learning are special cases of a general framework for federated stochastic approximation with Markovian noise, and we leverage this framework to provide a unified convergence analysis that applies to all the algorithms.
Information Theoretic Measures for Fairness-aware Feature Selection
In particular, we first propose information theoretic measures which quantify the impact of different subsets of features on the accuracy and discrimination of the decision outcomes.
Finite-Sample Analysis of Off-Policy Natural Actor-Critic with Linear Function Approximation
In this paper, we develop a novel variant of off-policy natural actor-critic algorithm with linear function approximation and we establish a sample complexity of $\mathcal{O}(\epsilon^{-3})$, outperforming all the previously known convergence bounds of such algorithms.
On the Linear convergence of Natural Policy Gradient Algorithm
We further improve this convergence result by introducing a variant of Natural Policy Gradient with adaptive step sizes.
Finite-Sample Analysis of Off-Policy Natural Actor-Critic Algorithm
In this paper, we provide finite-sample convergence guarantees for an off-policy variant of the natural actor-critic (NAC) algorithm based on Importance Sampling.
Impact of Data Processing on Fairness in Supervised Learning
Finally, we show that by appropriate choice of the discrimination measure, the optimization problem for both pre and post processing approaches will reduce to a linear program and hence can be solved efficiently.
Finite Sample Analysis of Two-Time-Scale Natural Actor-Critic Algorithm
In this paper, we characterize the \emph{global} convergence of an online natural actor-critic algorithm in the tabular setting using a single trajectory of samples.
Fairness in Supervised Learning: An Information Theoretic Approach
To ensure fairness and generalization simultaneously, we compress the data to an auxiliary variable, which is used for the prediction task.
