Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …

Thompson sampling is an algorithm for online decision problems where actions are taken
sequentially in a manner that must balance between exploiting what is known to maximize …

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 …

In recent years deep reinforcement learning (RL) systems have attained superhuman
performance in a number of challenging task domains. However, a major limitation of such …

Big Data applications are typically associated with systems involving large numbers of
users, massive complex software systems, and large-scale heterogeneous computing and …

Research on predictive processing models has focused largely on two specific algorithmic
theories: Predictive Coding for perception and Active Inference for decision-making. While …

Bayesian methods for machine learning have been widely investigated, yielding principled
methods for incorporating prior information into inference algorithms. In this survey, we …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …

The stochastic multi-armed bandit model is a simple abstraction that has proven useful in
many different contexts in statistics and machine learning. Whereas the achievable limit in …

The multi-armed bandit problem is a popular model for studying exploration/exploitation
trade-off in sequential decision problems. Many algorithms are now available for this well …