An Introduction to the Bootstrap.

By S. T. Buckland, Bradley Efron, Robert Tibshirani. 1994

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Deep Reinforcement Learning that Matters (2017, methodology): “tion), but the same principles can be applied to the online view. Conﬁdence Bounds The sample bootstrap has been a popular method to gain insight into a population distribution from a smaller sample (Efron and Tibshirani 1994). Bootstrap methods are particularly popular for A/B testing, and we can b”
Multi-Reward Reinforced Summarization with Saliency and Entailment (2018, neutral): “p < 0:001), and better than Entail-reward in all 4Our baseline is statistically equal to the paper-reported scores ofSee et al.(2017) (see Table1) on ROUGE-1, ROUGE-2, based on the bootstrap test (Efron and Tibshirani, 1994). Our baseline is stat. signiﬁcantly better (p < 0:001) in all ROUGE m”
Uncertainty-Aware Reinforcement Learning for Collision Avoidance (2017, methodology): “hniques that go beyond direct discriminative training. In order to obtain accurate uncertainty estimates from our model, we use two techniques: bootstrapping and dropout. Bootstrapping: Bootstrapping [5, 6] is a simple and effective method of estimating model uncertainty using resampling that can be”
Deep Exploration via Randomized Value Functions (2017, background): “re characteristics of the generating process. In its classic form, the bootstrap takes a dataset D of size N and generates a sampled dataset D~ also of size N drawn uniformly with replacement from D (Efron and Tibshirani, 1994). The bootstrap serves as a form of data-based simulation and, in certain”