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]: Online media offers opportunities to marketers to deliver brand messages to a large audience. Advertising technology platforms enables the advertisers to find the proper group of audiences and deliver ad impressions to them in real time. The recent growth of the real time bidding has posed a significant challenge on monitoring such a complicated system. With so many components we need a reliable system that detects the possible changes in the system and alerts the engineering team. In this paper we describe the mechanism that we invented for recovering the representative metrics and detecting the change in their behavior. We show that this mechanism is able to detect the possible problems in time by describing some incident cases.
[EX A]: Finding Needle in a Million Metrics: Anomaly Detection in a Large-scale Computational Advertising Platform

[EX Q]: Many real-world decision-theoretic planning problems can be naturally modeled with discrete and continuous state Markov decision processes (DC-MDPs). While previous work has addressed automated decision-theoretic planning for DCMDPs, optimal solutions have only been defined so far for limited settings, e.g., DC-MDPs having hyper-rectangular piecewise linear value functions. In this work, we extend symbolic dynamic programming (SDP) techniques to provide optimal solutions for a vastly expanded class of DCMDPs. To address the inherent combinatorial aspects of SDP, we introduce the XADD a continuous variable extension of the algebraic decision diagram (ADD) that maintains compact representations of the exact value function. Empirically, we demonstrate an implementation of SDP with XADDs on various DC-MDPs, showing the first optimal automated solutions to DCMDPs with linear and nonlinear piecewise partitioned value functions and showing the advantages of constraint-based pruning for XADDs.
[EX A]: Symbolic Dynamic Programming for Discrete and Continuous State MDPs

[EX Q]: <lb>We study the best arm identification (Best-1-Arm) problem, which is defined as follows.<lb>We are given n stochastic bandit arms. The ith arm has a reward distribution<lb>Di with an<lb>unknown mean i. Upon each play of the ith arm, we can get a reward, sampled i.i.d. from<lb>Di. We would like to identify the arm with the largest mean with probability at least 1 ,<lb>using as few samples as possible. We provide a nontrivial algorithm for Best-1-Arm, which<lb>improves upon several prior upper bounds on the same problem. We also study an important<lb>special case where there are only two arms, which we call the Sign- problem. We provide a<lb>new lower bound of Sign-, simplifying and significantly extending a classical result by Farrell<lb>in 1964, with a completely new proof. Using the new lower bound for Sign-, we obtain the<lb>first lower bound for Best-1-Arm that goes beyond the classic Mannor-Tsitsiklis lower bound,<lb>by an interesting reduction from Sign- to Best-1-Arm. We propose an interesting conjecture<lb>concerning the optimal sample complexity of Best-1-Arm from the perspective of instance-wise<lb>optimality.
[EX A]:
On the Optimal Sample Complexity for Best Arm Identification