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U.S. elections rely heavily on computers such as voter registration databases, electronic pollbooks, voting machines, scanners, tabulators, and results reporting websites. These introduce digital threats to election outcomes. Risk-limiting…

Cryptography and Security · Computer Science 2020-12-08 Amanda K. Glazer , Jacob V. Spertus , Philip B. Stark

Card-level comparison risk-limiting audits (CLCAs) heretofore required a CVR for each cast card and a "link" identifying which CVR is for which card -- which many voting systems cannot provide. Every set of CVRs that produces the same…

Applications · Statistics 2023-06-21 Philip B. Stark

Risk-limiting audits (RLAs) offer a statistical guarantee: if a full manual tally of the paper ballots would show that the reported election outcome is wrong, an RLA has a known minimum chance of leading to a full manual tally. RLAs…

Applications · Statistics 2018-09-13 Kellie Ottoboni , Philip B. Stark , Mark Lindeman , Neal McBurnett

Stratified sampling can be useful in risk-limiting audits (RLAs), for instance, to accommodate heterogeneous voting equipment or laws that mandate jurisdictions draw their audit samples independently. We combine the union-intersection tests…

Methodology · Statistics 2022-07-27 Jacob V. Spertus , Philip B. Stark

We present an approximate sampling framework and discuss how risk-limiting audits can compensate for these approximations, while maintaining their "risk-limiting" properties. Our framework is general and can compensate for counting mistakes…

Data Structures and Algorithms · Computer Science 2019-01-04 Mayuri Sridhar , Ronald L. Rivest

Risk-limiting post-election audits limit the chance of certifying an electoral outcome if the outcome is not what a full hand count would show. Building on previous work, we report on pilot risk-limiting audits in four elections during 2008…

The performance of a machine learning system is usually evaluated by using i.i.d.\ observations with true labels. However, acquiring ground truth labels is expensive, while obtaining unlabeled samples may be cheaper. Stratified sampling can…

Machine Learning · Computer Science 2019-07-29 Tiancheng Yu , Xiyu Zhai , Suvrit Sra

Risk-limiting audits (RLAs) can use information about which ballot cards contain which contests (card-style data, CSD) to ensure that each contest receives adequate scrutiny, without examining more cards than necessary. RLAs using CSD in…

Applications · Statistics 2023-09-19 Amanda K. Glazer , Jacob V. Spertus , Philip B. Stark

BRAVO, the most widely tried method for risk-limiting election audits, cannot accommodate sampling without replacement or stratified sampling, which can improve efficiency and may be required by law. It applies only to ballot-polling…

Methodology · Statistics 2022-08-15 Philip B. Stark

This article * provides an overview of post-election audit sampling research and compares various approaches to calculating post-election audit sample sizes, focusing on risklimiting audits, * discusses fundamental concepts common to all…

Applications · Statistics 2009-09-30 Kathy Dopp

Taiwan's auditors have suffered from processing excessive audit data, including drawing audit evidence. This study advances sampling techniques by integrating machine learning with sampling. This machine learning integration helps avoid…

Machine Learning · Computer Science 2024-03-22 Guang-Yih Sheu , Nai-Ru Liu

The main risk-limiting ballot polling audit in use today, BRAVO, is designed for use when single ballots are drawn at random and a decision regarding whether to stop the audit or draw another ballot is taken after each ballot draw…

Cryptography and Security · Computer Science 2021-02-23 Filip Zagórski , Grant McClearn , Sarah Morin , Neal McBurnett , Poorvi L. Vora

A collection of races in a single election can be audited as a group by auditing a random sample of batches of ballots and combining observed discrepancies in the races represented in those batches in a particular way: the maximum…

Applications · Statistics 2009-05-12 Philip B. Stark

Recent works have proposed optimal subsampling algorithms to improve computational efficiency in large datasets and to design validation studies in the presence of measurement error. Existing approaches generally fall into two categories:…

Methodology · Statistics 2025-12-25 Jasper B. Yang , Thomas Lumley , Bryan E. Shepherd , Pamela A. Shaw

Randomized smoothing (RS) has been shown to be a fast, scalable technique for certifying the robustness of deep neural network classifiers. However, methods based on RS require augmenting data with large amounts of noise, which leads to…

Machine Learning · Computer Science 2022-05-13 Ameya Joshi , Minh Pham , Minsu Cho , Leonid Boytsov , Filipe Condessa , J. Zico Kolter , Chinmay Hegde

An emerging class of data systems partition their data and precompute approximate summaries (i.e., sketches and samples) for each segment to reduce query costs. They can then aggregate and combine the segment summaries to estimate results…

Databases · Computer Science 2020-02-11 Edward Gan , Peter Bailis , Moses Charikar

For more than a century, election officials across the United States have inspected voting machines before elections using a procedure called Logic and Accuracy Testing (LAT). This procedure consists of election officials casting a test…

Cryptography and Security · Computer Science 2024-10-01 Braden L. Crimmins , J. Alex Halderman , Bradley Sturt

Risk-limiting audits (RLAs) can provide routine, affirmative evidence that reported election outcomes are correct by checking a random sample of cast ballots. An efficient RLA requires checking relatively few ballots. Here we construct…

Applications · Statistics 2024-10-16 Jacob Spertus

Document sketching using Jaccard similarity has been a workable effective technique in reducing near-duplicates in Web page and image search results, and has also proven useful in file system synchronization, compression and learning…

Data Structures and Algorithms · Computer Science 2014-10-17 Bernhard Haeupler , Mark Manasse , Kunal Talwar

Online controlled experiments, also known as A/B testing, are the digital equivalent of randomized controlled trials for estimating the impact of marketing campaigns on website visitors. Stratified sampling is a traditional technique for…

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