We study offline model-based optimization to maximize a black-box objective function with a static dataset of designs and scores.

Recent works in V ariational Inference have examined alternative criteria to the commonly used exclusive Kullback-Leibler divergence.

Does knowledge distillation really work?

The power of DNNs relies heavily on the quantity and quality of training data. However, collecting and annotating data on a large scale is often expensive and time-consuming.

Unsafe operations described in natural text lead to a lower rejection rate than thoseincodeformat.

Nonetheless, modern machine learning is adaptive in its nature. Prior information about a model's performance on the test set inevitably influences

However, while popular, the system's suitability for assessing entities with constant skill levels, such as LLMs, remains relatively unexplored. We study two fundamental axioms that evaluation methods should adhere to: reliability and transitivity .