The prevalent deployment of learning from human preferences through reinforcement
learning (RLHF) relies on two important approximations: the first assumes that pairwise …

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 …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of
intelligent objects, such as animated statues of human beings and tables that arrive full of …

Multi-armed bandit problems are the predominant theoretical model of exploration-
exploitation tradeoffs in learning, and they have countless applications ranging from medical …

Content caching in small base stations or wireless infostations is considered to be a suitable
approach to improve the efficiency in wireless content delivery. Placing the optimal content …

Mobile sensing systems have made significant advances in tracking human behavior.
However, the development of personalized mobile health feedback systems is still in its …

Internet of Vehicles (IoV) is a distributed network of connected cars, roadside infrastructure,
wireless communication networks, and central cloud platforms. Wireless recommendations …

The first paper on contextual bandits was written by Michael Woodroofe in 1979 (Journal of
the American Statistical Association, 74 (368), 799–806, 1979) but the term “contextual …

In a multi-armed bandit (MAB) problem, an online algorithm makes a sequence of choices.
In each round it chooses from a time-invariant set of alternatives and receives the payoff …