In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current mod­ els that combine both primitive actions and macro-actions and leave the state space un­ changed, we propose a hierarchical model (using an abstract MDP) that works with macro-actions only, and that significantly reduces the size of the state space. This is achieved by treating macro­ actions as local policies that act in certain regions of state space, and by restricting states in the ab­ stract MDP to those at the boundaries of regions. The abstract MDP approximates the original and can be solved more efficiently. We discuss sev­ eral ways in which macro-actions can be gen­ erated to ensure good solution quality. Finally, we consider ways in which macro-actions can be reused to solve multiple, related MDPs; and we show that this can justify the computational over­ head of macro-action generation.
[EX A]: Hierarchical Solution of Markov Decision Processes using Macro-actions

[EX Q]: We consider the stochastic approximation problem where a convex function has to be minimized, given only the knowledge of unbiased estimates of its gradients at certain points, a framework which includes machine learning methods based on the minimization of the empirical risk. We focus on problems without strong convexity, for which all previously known algorithms achieve a convergence rate for function values of O(1/ √
[EX A]: Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)

[EX Q]: Or’s of And’s (OA) models are comprised of a small number of disjunctions of conjunctions, also called disjunctive normal form. An example of an OA model is as follows: If (x1 = ‘blue’ AND x2 = ‘middle’) OR (x1 = ‘yellow’), then predict Y = 1, else predict Y = 0. Or’s of And’s models have the advantage of being interpretable to human experts, since they are a set of conditions that concisely capture the characteristics of a specific subset of data. We present two optimization-based machine learning frameworks for constructing OA models, Optimized OA (OOA) and its faster version, Optimized OA with Approximations (OOAx). We prove theoretical bounds on the properties of patterns in an OA model. We build OA models as a diagnostic screening tool for obstructive sleep apnea, that achieves high accuracy with a substantial gain in interpretability over other methods.
[EX A]:
Learning Optimized Or’s of And’s