Computerized adaptive testing (CA T), as a tool that can efficiently measure student's ability, has been widely used in various standardized tests (e.g., GMA T and

A finite episodic Markov decision process (MDP) is a tuple (S,A,H,α,P,r) where S and A are the finite sets of states and actions withS = |S|,A = |A|, H is the (fixed) episode length andα is the initial state distribution.

Whilestochastic optimization ofAUROChasbeenstudied extensively, principled stochastic optimization of AUPRC has been rarely explored.

We study the problem of learning a linear model to set the reserve price in an auction, given contextual information, in order to maximize expected revenue fromtheseller side.

Most previous studies focus on the independent cascade (IC) model under the edge-level feedback.