Residential loads have great potential to enhance the efficiency and reliability of electricity
systems via demand response (DR) programs. One major challenge in residential DR is …

Recent advances in Deep Neural Networks (DNNs) have dramatically improved the
accuracy of DNN inference, but also introduce larger latency. In this paper, we investigate …

The implementation of efficient demand response (DR) programs for household electricity
consumption would benefit from data-driven methods capable of simulating the impact of …

Finding an optimal individualized treatment regimen is considered one of the most
challenging precision medicine problems. Various patient characteristics influence the …

We consider a finite-horizon Mean Field Control problem for Markovian models. The
objective function is composed of a sum of convex and Lipschitz functions taking their values …

We address the problem of stochastic combinatorial semi-bandits, where a player selects
among $ P $ actions from the power set of a set containing $ d $ base items. Adaptivity to the …

We address the problem of stochastic combinatorial semi-bandits, where a player can select
from P subsets of a set containing d base items. Most existing algorithms (eg CUCB, ESCB …

Integrating renewable energy into the power grid while balancing supply and demand is a
complex issue, given its intermittent nature. Demand side management (DSM) offers …

With a large-scale integration of renewable generation, especially wind power and solar
energy, significant uncertainty and volatility have been introduced to the control and …

Multi-armed bandits (MAB) are extensively studied in various settings where the objective is
to\textit {maximize} the actions' outcomes (ie, rewards) over time. Since safety is crucial in …