Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

We design and analyse variations of the classical Thompson sampling (TS) procedure for
Bayesian optimisation (BO) in settings where function evaluations are expensive but can be …

Federated Learning is an emerging direction in distributed machine learning that en-ables
jointly training a model without sharing the data. Since the data is distributed across many …

How can we take advantage of opportunities for experimental parallelization in
explorationexploitation tradeoffs? In many experimental scenarios, it is often desirable to …

Cooperative offloading in mobile edge computing enables resource-constrained edge
clouds to help each other with computation-intensive tasks. However, the power of such …

In this paper, we study Federated Bandit, a decentralized Multi-Armed Bandit problem with a
set of N agents, who can only communicate their local data with neighbors described by a …

Fraud detection and prevention play an important part in ensuring the sustained operation of
any e-commerce business. Machine learning (ML) often plays an important role in these anti …

In this paper, we consider online learning in generalized linear contextual bandits where
rewards are not immediately observed. Instead, rewards are available to the decision maker …

We study a decentralized cooperative stochastic multi-armed bandit problem with K arms on
a network of N agents. In our model, the reward distribution of each arm is the same for each …

We study a variant of the stochastic $ K $-armed bandit problem, which we call" bandits with
delayed, aggregated anonymous feedback”. In this problem, when the player pulls an arm, a …