Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

In this paper, we consider the problem of Gaussian process (GP) optimization with an added
robustness requirement: The returned point may be perturbed by an adversary, and we …

The objective of this work is to augment the basic abilities of a robot by learning to use
sensorimotor primitives to solve complex long-horizon manipulation problems. This requires …

We consider the problem of optimizing a black-box function based on noisy bandit feedback.
Kernelized bandit algorithms have shown strong empirical and theoretical performance for …

How can we efficiently gather information to optimize an unknown function, when presented
with multiple, mutually dependent information sources with different costs? For example …

Robustness to distributional shift is one of the key challenges of contemporary machine
learning. Attaining such robustness is the goal of distributionally robust optimization, which …

Many black-box optimization tasks arising in high-stakes applications require risk-averse
decisions. The standard Bayesian optimization (BO) paradigm, however, optimizes the …

Consider the sequential optimization of a continuous, possibly non-convex, and expensive
to evaluate objective function $ f $. The problem can be cast as a Gaussian Process (GP) …

Robust machine learning is currently one of the most prominent topics which could
potentially help sha** a future of advanced AI platforms that not only perform well in …

In this paper, we consider the problem of sequentially optimizing a black-box function $ f $
based on noisy samples and bandit feedback. We assume that $ f $ is smooth in the sense …