Stochastic optimization is an umbrella term that includes over a dozen fragmented
communities, using a patchwork of sometimes overlapping notational systems with …

Any sufficiently complex system acts as a black box when it becomes easier to experiment
with than to understand. Hence, black-box optimization has become increasingly important …

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∈ …

Companies such as Zara and World Co. have recently implemented novel product
development processes and supply chain architectures enabling them to make more …

We derive a one-period look-ahead policy for finite-and infinite-horizon online optimal
learning problems with Gaussian rewards. Our approach is able to handle the case where …

We consider a two–armed bandit problem which involves sequential sampling from two non-
homogeneous populations. The response in each is determined by a random covariate …

A long-lived autonomous agent should be able to respond online to novel instances of tasks
from a familiar domain. Acting online requires 'fast'responses, in terms of rapid convergence …

We consider a multiarmed bandit problem where the expected reward of each arm is a
linear function of an unknown scalar with a prior distribution. The objective is to choose a …

The looming end of Moore's Law and ascending use of deep learning drives the design of
custom accelerators that are optimized for specific neural architectures. Architecture …

Stochastic optimization, also known as optimization under uncertainty, is studied by over a
dozen communities, often (but not always) with different notational systems and styles …