Hypothesis testing for high-dimensional multinomials: A selective review

Balakrishnan, Sivaraman; Wasserman, Larry

doi:10.1214/18-aoas1155sf

Cited by 51 publications

(48 citation statements)

References 57 publications

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“…Remark: Here we are specifically interested in whether the distribution of victims For a recent overview of the difficulties and various approaches to dealing with these and related high-dimensional testing issues, we refer the interested reader to [34].…”

Section: Body Camera Effectsmentioning

confidence: 99%

On Racial Disparities in Recent Fatal Police Shootings

Mentch

2020

Statistics and Public Policy

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Fatal police shootings in the United States continue to be a polarizing social and political issue. Clear disagreement between racial proportions of victims and nationwide racial demographics together with graphic video footage has created fertile ground for controversy. However, simple population level summary statistics fail to take into account fundamental local characteristics such as county-level racial demography, local arrest demography, and law enforcement density. Utilizing data on fatal police shootings between January 2015 and July 2016, we implement a number of straightforward resampling procedures designed to carefully examine how unlikely the victim totals from each race are with respect to these local population characteristics if no racial bias were present in the decision to shoot by police. We present several approaches considering the shooting locations both as fixed and also as a random sample. In both cases, we find overwhelming evidence of a racial disparity in shooting victims with respect to local population demographics but substantially less disparity after accounting for local arrest demographics. We conclude our analyses by examining the effect of police-worn body cameras and find no evidence that the presence of such cameras impacts the racial distribution of victims.

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Section: Body Camera Effectsmentioning

confidence: 99%

On Racial Disparities in Recent Fatal Police Shootings

Mentch

2020

Statistics and Public Policy

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“…Our proposed evaluation metric is closely related to the total variation distance which has been studied extensively in the distribution testing literature. It is known that total variation distance estimates have pessimistic minimax estimation rates in high dimensions (Balakrishnan and Wasserman, 2017). Our work overcomes this by utilizing p model and an estimate of p ref .…”

Section: Distributional Divergence Estimationmentioning

confidence: 96%

Unifying Human and Statistical Evaluation for Natural Language Generation

Hashimoto¹,

Zhang²,

Liang³

2019

Proceedings of the 2019 Conference of the North

131

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How can we measure whether a natural language generation system produces both high quality and diverse outputs? Human evaluation captures quality but not diversity, as it does not catch models that simply plagiarize from the training set. On the other hand, statistical evaluation (i.e., perplexity) captures diversity but not quality, as models that occasionally emit low quality samples would be insufficiently penalized. In this paper, we propose a unified framework which evaluates both diversity and quality, based on the optimal error rate of predicting whether a sentence is human-or machine-generated. We demonstrate that this error rate can be efficiently estimated by combining human and statistical evaluation, using an evaluation metric which we call HUSE. On summarization and chitchat dialogue, we show that (i) HUSE detects diversity defects which fool pure human evaluation and that (ii) techniques such as annealing for improving quality actually decrease HUSE due to decreased diversity. 1 While some tasks only care about quality and thus only require pmodel to place mass on some high quality y, we demand that pmodel places mass on all high quality y as given by pref.

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“…Many theories and techniques in statistics, such as hypothesis testing or confidence measures [307], can be applied in this research. There are more studies on the reliability estimation of the results in traditional statistics and other application fields [308]- [310], but it does not seem to have attracted enough attention in the field of Android malware detection. This phenomenon can be attributed to the fact that most researchers focus their evaluation on the performance of classifiers, and the fact that many studies are based on an established and labeled dataset, without considering the use of classifiers in real-world environments [283].…”

Section: E Classifier Evaluationmentioning

confidence: 99%

A Review of Android Malware Detection Approaches Based on Machine Learning

et al. 2020

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Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. We believe our work complements the previous reviews by surveying a wider range of aspects of the topic. This paper presents a comprehensive survey of Android malware detection approaches based on machine learning. We briefly introduce some background on Android applications, including the Android system architecture, security mechanisms, and classification of Android malware. Then, taking machine learning as the focus, we analyze and summarize the research status from key perspectives such as sample acquisition, data preprocessing, feature selection, machine learning models, algorithms, and the evaluation of detection effectiveness. Finally, we assess the future prospects for research into Android malware detection based on machine learning. This review will help academics gain a full picture of Android malware detection based on machine learning. It could then serve as a basis for subsequent researchers to start new work and help to guide research in the field more generally.

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Hypothesis testing for high-dimensional multinomials: A selective review

Cited by 51 publications

References 57 publications

On Racial Disparities in Recent Fatal Police Shootings

On Racial Disparities in Recent Fatal Police Shootings

Unifying Human and Statistical Evaluation for Natural Language Generation

A Review of Android Malware Detection Approaches Based on Machine Learning

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