While experimentation is essential, traditional A/B testing can be excessively slow and expensive, according to DoorDash engineers Caixia Huang and Alex Weinstein. To address these limitations, they ...

In the domain of metamaterials, the push toward automated design has been accelerated by advances in generative machine learning. The advent of deep ...

Abstract: Safe Bayesian optimization (BO) with Gaussian processes is an effective tool for tuning control policies in safety-critical real-world systems, specifically due to its sample efficiency and ...

The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches to improve ...

Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

These open-source MMM tools solve different measurement problems, from budget optimization to forecasting and preprocessing.

Bayesian SSH transforms your SSH experience with intelligent automation: ...

Abstract: Bayesian optimization is commonly used to optimize black-box functions associated with simulations in engineering and science. Bayesian optimization contains two essential components: the ...

State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, China State Key Laboratory of Clean and Efficient Turbomachinery Power Equipment, Department of Mechanical ...

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