Pure exploration in multi-armed bandits with low rank structure using oblivious sampler

In this paper, we consider the low rank structure of the reward sequence of the pure exploration problems. Firstly, we propose the separated setting in pure exploration problem, where the exploration strategy cannot receive the feedback of its explorations. Due to this separation, it requires that the exploration strategy to sample the arms obliviously. By involving the kernel information of the reward vectors, we provide efficient algorithms for both time-varying and fixed cases with regret bound $O(d\sqrt{(\ln N)/n})$. Then, we show the lower bound to the pure exploration in multi-armed bandits with low rank sequence. There is an $O(\sqrt{\ln N})$ gap between our upper bound and the lower bound.Comment: 15 page

Gaussian Process Classification Bandits

Classification bandits are multi-armed bandit problems whose task is to classify a given set of arms into either positive or negative class depending on whether the rate of the arms with the expected reward of at least h is not less than w for given thresholds h and w. We study a special classification bandit problem in which arms correspond to points x in d-dimensional real space with expected rewards f(x) which are generated according to a Gaussian process prior. We develop a framework algorithm for the problem using various arm selection policies and propose policies called FCB and FTSV. We show a smaller sample complexity upper bound for FCB than that for the existing algorithm of the level set estimation, in which whether f(x) is at least h or not must be decided for every arm's x. Arm selection policies depending on an estimated rate of arms with rewards of at least h are also proposed and shown to improve empirical sample complexity. According to our experimental results, the rate-estimation versions of FCB and FTSV, together with that of the popular active learning policy that selects the point with the maximum variance, outperform other policies for synthetic functions, and the version of FTSV is also the best performer for our real-world dataset

The Impact of Medical Students Teaching Basic Life Support to Laypersons

Basic life support (BLS) courses for laypersons, including cardiopulmonary resuscitation (CPR) training, is known to improve outcomes of out-of-hospital cardiac events. We asked medical students to provide BLS training for laypersons as a part of their emergency medicine education and evaluated the effects of training on the BLS skills of laypersons. We also used a questionnaire to determine whether the medical students who provided the BLS training were themselves more confident and motivated to perform BLS compared to students who did not provide BLS training. The proportions of laypersons who reported confidence in checking for a response, performing chest compressions, and automated external defibrillator (AED) use were significantly increased after the BLS training. The proportions of medical students who reported increased confidence/motivation in terms of understanding BLS, checking for a response, chest compression, use of AED, and willingness to perform BLS were significantly greater among medical students who provided BLS instructions compared to those who did not. BLS instruction by medical students was associated with an improvement in laypersons’ CPR accuracy and confidence in responding to cardiac arrest. The results indicate that medical students could gain understanding, confidence, and motivation in regard to their BLS skills by teaching BLS to laypersons