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 …

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

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 …

Recent efforts applying machine learning techniques to query optimization have shown few
practical gains due to substantive training overhead, inability to adapt to changes, and poor …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

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 …

We introduce a new, efficient, principled and backpropagation-compatible algorithm for
learning a probability distribution on the weights of a neural network, called Bayes by …

We consider the stochastic bandit problem with a continuous set of arms, with the expected
reward function over the arms assumed to be fixed but unknown. We provide two new …

Motivated by the task of hyperparameter optimization, we introduce the\em non-stochastic
best-arm identification problem. We identify an attractive algorithm for this setting that makes …

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