A long-standing goal of machine-learning-based protein engineering is to accelerate the
discovery of novel mutations that improve the function of a known protein. We introduce a …

Many problems in science and engineering involve optimizing an expensive black-box
function over a high-dimensional space. For such black-box optimization (BBO) problems …

While machine learning models are typically trained to solve prediction problems, we might
often want to use them for optimization problems. For example, given a dataset of proteins …

Offline design optimization problem arises in numerous science and engineering
applications including material and chemical design, where expensive online …

The design of novel protein sequences is providing paths towards the development of novel
therapeutics and materials. At the forefront is the challenging field of de novo protein design …

We consider offline policy optimization (OPO) in contextual bandits, where one is given a
fixed dataset of logged interactions. While pessimistic regularizers are typically used to …

Variational quantum algorithms stand at the forefront of simulations on near-term and future
fault-tolerant quantum devices. While most variational quantum algorithms involve only …

Offline optimization is an important task in numerous material engineering domains where
online experimentation to collect data is too expensive and needs to be replaced by an in …

Offline data-driven evolutionary algorithms (DDEAs), which learn problem models from
historical data and then perform optimization, have attracted significant attention in the data …

Addressing real-world optimization problems becomes particularly challenging when
analytic objective functions or constraints are unavailable. While numerous studies have …