Given a water distribution network, where should we place sensors toquickly detect contaminants?
Or, which blogs should we read to avoid missing important stories?. These seemingly …

Information diffusion and virus propagation are fundamental processes taking place in networks.
While it is often possible to directly observe when nodes become infected with a virus or …

Many applications require optimizing an unknown, noisy function that is expensive to
evaluate. We formalize this task as a multi-armed bandit problem, where the payoff function is …

When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs),
choosing sensor locations is a fundamental task. There are several common …

Submodularity1 is a property of set functions with deep theoretical consequences and far–reaching
applications. At first glance it appears very similar to concavity, in other ways it …

We tackle the fundamental problem of Bayesian active learning with noise, where we need
to adaptively select from a number of expensive tests in order to identify an unknown …

Reinforcement learning is a powerful paradigm for learning optimal policies from experimental
data. However, to find optimal policies, most reinforcement learning algorithms explore all …

How should we design experiments to maximize performance of a complex system, taking
into account uncontrollable environmental conditions? How should we select relevant …

Many problems in artificial intelligence require adaptively making a sequence of decisions
with uncertain outcomes under partial observability. Solving such stochastic optimization …

Following the events of September 11, 2001, in the United States, world public awareness
for possible terrorist attacks on water supply systems has increased dramatically. Among the …