Computer-intensive methods for testing hypotheses

By E. Hollas, Eric W. Noreen. 1989

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Citations in the corpus

Efficient Large Scale Language Modeling with Mixtures of Experts (2021, background): “05) difference between best and worst scores based on bootstrap test (Noreen, 1989; Efron and Tibshirani, 1994).”
Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning (2018, methodology): “6 We test for statistical significance with approximate randomization (Noreen, 1989).”
Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition (2003, methodology): “For all the F β =1 rates we have estimated sig-niﬁcance boundaries by using bootstrap resampling (Noreen, 1989).”
Automatically Learning Data Augmentation Policies for Dialogue Tasks (2019, methodology): “Statistical significance is computed via the bootstrap test (Noreen, 1989) over 100K samples.”
Continual and Multi-Task Architecture Search (2019, neutral): “01, and statistically equal on RTE and WNLI (where the validations sets are very small), based on the bootstrap test (Noreen, 1989; Efron and Tibshirani, 1994) with 100K samples.”
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting (2018, methodology): “We calculate statistical significance based on the bootstrap test (Noreen, 1989; Efron and Tibshirani, 1994) with 100K samples.”
GraDA: Graph Generative Data Augmentation for Commonsense Reasoning (2022, methodology): “Statistical significance is computed with 100K samples using bootstrap (Noreen, 1989; Tibshirani and Efron, 1993).”
Reinforced Video Captioning with Entailment Rewards (2017, methodology): “05 for BLEU, based on the bootstrap test (Noreen, 1989; Efron and Tibshirani, 1994).”
Soft Layer-Specific Multi-Task Summarization with Entailment and Question Generation (2018, methodology): “answer-span prediction classifier from Pasunuru and Bansal (2018) trained on SQuAD (Rajpurkar et al.”
Bandit Structured Prediction for Neural Sequence-to-Sequence Learning (2017, methodology): “Objective (1) is known from minimum risk training (Och, 2003) and has been lifted to NMT by Shen et al. (2016) – but not for learning from weak feedback.”
On the Importance of Text Analysis for Stock Price Prediction (2014, methodology): “We used approximate randomization (Hoeffding, 1952; Noreen, 1989) for checking statistical significance.”
GraDA: Graph Generative Data Augmentation for Commonsense Reasoning (2022, methodology): “Statistical significance is computed with 100K samples using bootstrap (Noreen, 1989; Tibshirani and Efron, 1993).”
Polite Dialogue Generation Without Parallel Data (2018, methodology): “significance via the bootstrap test (Noreen, 1989; Efron and Tibshirani, 1994) with 100K samples.”
Securities Price Consequences of the Private Securities Litigation Reform Act of 1995 and Related Events (2001, unknown): “To control for this bias, we conduct a randomization test (Noreen 1989).”
A paired comparison approach for the analysis of sets of Likert-scale responses (2007, methodology): “To obtain an understanding of how well the model fits the data, we simulated the distribution Statistical Modelling 2007; 7(1): 3–28 of the deviance by a Monte Carlo test procedure (see e.g. Noreen, 1989).”
Commonsense for Generative Multi-Hop Question Answering Tasks (2018, methodology): “significance computed using bootstrap test with 100K iterations (Noreen, 1989; Efron and Tibshirani, 1994).”
When Choosing Plausible Alternatives, Clever Hans can be Clever (2019, methodology): “p-values according to Approximate Randomization Tests (Noreen, 1989), with ∗ indicating a significant difference between performance on Easy and Hard p < 5%.”
Adversarial Over-Sensitivity and Over-Stability Strategies for Dialogue Models (2018, methodology): “significance via the bootstrap test (Noreen, 1989; Efron and Tibshirani, 1994) with 100K samples, and consider p < 0.”