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Found 6 papers, 1 papers with code
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems
This paper studies the Bayesian regret of a variant of the Thompson-Sampling algorithm for bandit problems.
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards
In this work, we study the performance of the Thompson Sampling algorithm for Contextual Bandit problems based on the framework introduced by Neu et al. and their concept of lifted information ratio.
An Information-Theoretic Analysis of Bayesian Reinforcement Learning
Building on the framework introduced by Xu and Raginksy [1] for supervised learning problems, we study the best achievable performance for model-based Bayesian reinforcement learning problems.
Decentralized Differentially Private Segmentation with PATE
For this reason, we turn our attention to Private Aggregation of Teacher Ensembles (PATE), where all local models can be trained independently without inter-institutional communication.
On the Minimum Achievable Age of Information for General Service-Time Distributions
There has been a significant research effort in optimizing this metric in communication and networking systems under different settings.
Privacy-preserving smart meter control strategy including energy storage losses
In this paper, we present a one-step-ahead predictive control strategy using Bayesian risk to measure and control privacy leakage with an energy storage system.
Signal Processing
