Search Results for author: Tobias J. Oechtering

Found 6 papers, 1 papers with code

Privacy-preserving smart meter control strategy including energy storage losses

1 code implementation21 Mar 2018 Ramana R. Avula, Tobias J. Oechtering, Daniel Månsson

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

On the Minimum Achievable Age of Information for General Service-Time Distributions

no code implementations19 Jan 2020 Jaya Prakash Champati, Ramana R. Avula, Tobias J. Oechtering, James Gross

There has been a significant research effort in optimizing this metric in communication and networking systems under different settings.

Decentralized Differentially Private Segmentation with PATE

no code implementations10 Apr 2020 Dominik Fay, Jens Sjölund, Tobias J. Oechtering

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.

Brain Tumor Segmentation Federated Learning +2

An Information-Theoretic Analysis of Bayesian Reinforcement Learning

no code implementations18 Jul 2022 Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, Mikael Skoglund

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.

reinforcement-learning Reinforcement Learning (RL)

Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards

no code implementations26 Apr 2023 Amaury Gouverneur, Borja Rodríguez-Gálvez, Tobias J. Oechtering, Mikael Skoglund

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.

Multi-Armed Bandits Thompson Sampling

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