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Found 7 papers, 2 papers with code
Best-Arm Identification in Correlated Multi-Armed Bandits
In this paper we consider the problem of best-arm identification in multi-armed bandits in the fixed confidence setting, where the goal is to identify, with probability $1-\delta$ for some $\delta>0$, the arm with the highest mean reward in minimum possible samples from the set of arms $\mathcal{K}$.
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning
Due to communication constraints and intermittent client availability in federated learning, only a subset of clients can participate in each training round.
Multi-Armed Bandits with Correlated Arms
We consider a multi-armed bandit framework where the rewards obtained by pulling different arms are correlated.
A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting
We consider a finite-armed structured bandit problem in which mean rewards of different arms are known functions of a common hidden parameter $\theta^*$.
On the Evolution of Spreading Processes in Complex Networks
In this paper, we investigate the evolution of spreading processes on complex networks with the aim of i) revealing the role of evolution on the threshold, probability, and final size of epidemics; and ii) exploring the interplay between the structural properties of the network and the dynamics of evolution.
Physics and Society Social and Information Networks
Correlated Multi-armed Bandits with a Latent Random Source
As a result, there are regimes where our algorithm achieves a $\mathcal{O}(1)$ regret as opposed to the typical logarithmic regret scaling of multi-armed bandit algorithms.
Active Distribution Learning from Indirect Samples
At each time step, we choose one of the possible $K$ functions, $g_1, \ldots, g_K$ and observe the corresponding sample $g_i(X)$.
