Deep neural networks are becoming increasingly powerful and large always require more labelled data to be trained. However, since annotating is time-consuming, it now necessary develop systems that show good performance while learning on a limited amount of data. These must correctly chosen obtain models still efficient. For this, the able determine which should annotated achieve best results. ...

Many sequential decision-making systems leverage data collected using prior policies to propose a new policy. For critical applications, it is important that high-confidence guarantees on the policy’s behavior are provided before deployment, ensure policy will behave as desired. Prior works have studied off-policy estimation of expected return, however, variance returns can be equally for high-...

In this paper, we study the estimation problems for the Burr type III distribution based on a complete sample. The maximum likelihood method is used to derive the point estimators of the parameter. An exact confidence interval and an exact joint confidence region for the parameters are constructed. Two numerical examples with real data set and simulated data, are presented to illustrate the met...

Let p1 ≤ p2 and consider estimating a fixed set {x : p1 ≤ f(x) ≤ p2} by the random set {x : p1 ≤ f̂n(x) ≤ p2}, where f̂n is a consistent estimator of the continuous function f . This paper gives consistency conditions for these sets, and provides a new method to construct confidence regions from empirical averages of sets. The method can also be used to construct confidence regions for sets of th...