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 …

An up-to-date account of the interplay between optimization and machine learning,
accessible to students and researchers in both communities. The interplay between …

We study the average cost Linear Quadratic (LQ) control problem with unknown model
parameters, also known as the adaptive control problem in the control community. We …

We consider bandit problems involving a large (possibly infinite) collection of arms, in which
the expected reward of each arm is a linear function of an r-dimensional random vector Z∈ …

Bandit convex optimization is a special case of online convex optimization with partial
information. In this setting, a player attempts to minimize a sequence of adversarially …

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 …

We study sequential prediction problems in which, at each time instance, the forecaster
chooses a vector from a given finite set S⊆ Rd. At the same time, the opponent chooses a …

In a multi-armed bandit problem, an online algorithm chooses from a set of strategies in a
sequence of n trials so as to maximize the total payoff of the chosen strategies. While the …

A central problem in sequential decision making is to develop algorithms that are practical
and computationally efficient, yet support the use of flexible, general-purpose models …

Competing in the Dark: An Efficient Algorithm for Bandit Linear Optimization Page 1 Competing
in the Dark: An Efficient Algorithm for Bandit Linear Optimization Jacob Abernethy Computer …