Simultaneously optimizing many design parameters in time-consuming experiments causes
bottlenecks in a broad range of scientific and engineering disciplines,. One such example is …

The goal of offline black-box optimization (BBO) is to optimize an expensive black-box
function using a fixed dataset of function evaluations. Prior works consider forward …

Abstract Machine learning (ML) has been recently introduced to solving optimization
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …

Computational sustainability: computing for a better world and a sustainable future Page 1 56
COMMUNICATIONS OF THE ACM | SEPTEMBER 2019 | VOL. 62 | NO. 9 Computational …

In this paper, we consider online learning in generalized linear contextual bandits where
rewards are not immediately observed. Instead, rewards are available to the decision maker …

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) …

Advanced rechargeable battery technologies are the primary source of energy storage,
which hold significant promise for tackling energy challenges. However, the progress of …

Recommender systems are a ubiquitous feature of online platforms. Increasingly, they are
explicitly tasked with increasing users' long-term satisfaction. In this context, we study a …

Many problems in science and engineering involve optimizing an expensive black-box
function over a high-dimensional space. In the offline model-based optimization (MBO) …

Stochastic linear bandits are a natural and well-studied model for structured exploration/
exploitation problems and are widely used in applications such as on-line marketing and …