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 …

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 …

We consider derivative-free algorithms for stochastic and nonstochastic convex optimization
problems that use only function values rather than gradients. Focusing on nonasymptotic …

Stochastic gradient descent (SGD) is a simple and popular method to solve stochastic
optimization problems which arise in machine learning. For strongly convex problems, its …

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

This paper considers online convex optimization (OCO) with stochastic constraints, which
generalizes Zinkevich's OCO over a known simple fixed set by introducing multiple …

We consider the adversarial linear contextual bandit problem, where the loss vectors are
selected fully adversarially and the per-round action set (ie the context) is drawn from a fixed …

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 …

We consider the problem of preference-based reinforcement learning (PbRL), where, unlike
traditional reinforcement learning (RL), an agent receives feedback only in terms of 1 bit …

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 …