Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance.

In recent years, machine learning has shown great promise in a variety of real-world applications.

Uncertainty quantification (UQ) is important for reliability assessment and enhancement of machine learning models.

We extensively study the design space of these embeddings and highlight important design considerations.

Our estimators assume that there is no interference between the units, i.e., that the treatment of one The first assumption is quite realistic as in most peer review systems the reviewers cannot see other reviews until they submit their own. The second assumption is important to understand, as there could be "batch effects": a Monte Carlo methods to tightly estimate these covariances. AAAI datasets, we sampled 1 million assignments and computed the empirical covariance. In our setting, small amounts of attrition (relative to the number of policy-induced positivity violations) mean that the fraction of data that is missing is not exactly known before assignment, but almost. To get more robust estimates of the performance, we repeat this process 10 times.