Search algorithms for the bandit problems are applicable in materials discovery. However, the objectives of the conventional bandit problem are different from those of materials discovery. The conventional bandit problem aims to maximize the total rewards, whereas materials discovery aims to achieve breakthroughs in material properties. The max K-armed bandit (MKB) problem, which aims to acquire the single best reward, matches with the discovery tasks better than the conventional bandit. Thus, here, we propose a search algorithm for materials discovery based on the MKB problem using a pseudo-value of the upper confidence bound of expected improvement of the best reward. This approach is pseudo-guaranteed to be asymptotic oracles that do not depends on the time horizon. In addition, compared with other MKB algorithms, the proposed algorithm has only one hyperparameter, which is advantageous in materials discovery. We applied the proposed algorithm to synthetic problems and molecular-design demonstrations using a Monte Carlo tree search. According to the results, the proposed algorithm stably outperformed other bandit algorithms in the late stage of the search process when the optimal arm of the MKB could not be determined based on its expectation reward.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Over the last two years, one of the most common ways for organizations to scale and run increasingly large and complex artificial intelligence (AI) workloads has been with the open-source Ray framework, used by companies from OpenAI to Shopify and Instacart. Ray enables machine learning (ML) models to scale across hardware resources and can also be used to support MLops workflows across different ML tools. Ray 1.0 came out in September 2020 and has had a series of iterations over the last two years. Today, the next major milestone was released, with the general availability of Ray 2.0 at the Ray Summit in San Francisco.

There are many reasons why Python has emerged as the number one language for data science. It's easy to get started and relatively forgiving for beginners, yet it's also powerful and extensible enough for experts to take on complex tasks. But there's one aspect of Python that has bedeviled developers in the big data age: Getting Python to scale past a single node. Solving that dilemma is the number one goal of Project Ray. The name "Ray" will ring a bell if you've been following the goings-on at RISELab, the advanced computing laboratory formed at UC Berkeley.

Probabilistic models play a central role in modern machine learning. They offer a powerful framework for learning from data, and they have found applications in a variety of scientific fields beyond machine learning. A longstanding goal in machine learning and statistics is to achieve a separation between modeling and inference, so that users of these tools may focus on specifying models without having to implement new inference algorithms every time the models change. Recently, work in probabilistic programming has taken up this challenge, seeking to unify probabilistic modeling with computer programming in order to dramatically increase the effectiveness of machine learning experts (DARPA, 2013) and to equip non-experts with effective tools for specifying models and performing inference. We anticipate that continued success toward these goals will decrease the reliance of machine learning practitioners on tried-and-true models and will shift the community toward a paradigm grounded in flexible tools for rapidly prototyping and designing new models (Bishop, 2013).