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 often have parameters that can be translated, scaled, permuted, or otherwise transformed without changing the model. These symmetries can lead to strong correlation and multimodality in the posterior distribution over the model's parameters, which can pose challenges both for performing inference and interpreting the results. In this work, we address the automatic detection of common problematic model symmetries. To do so, we introduce local symmetries, which cover many common cases and are amenable to automatic detection. We show how to derive algorithms to detect several broad classes of local symmetries. Our algorithms are compatible with probabilistic programming constructs such as arrays, for loops, and if statements, and they scale to models with many variables.