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Brandon R. Anderson
Researcher at Stanford University
Publications - 7
Citations - 108
Brandon R. Anderson is an academic researcher from Stanford University. The author has contributed to research in topics: Matching (statistics) & Change detection. The author has an hindex of 2, co-authored 6 publications receiving 13 citations.
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Proceedings ArticleDOI
When does pretraining help?: assessing self-supervised learning for law and the CaseHOLD dataset of 53,000+ legal holdings
TL;DR: In this article, the authors present a new dataset called Case Holdings On Legal Decisions (CaseHOLD), which consists of over 53,000+ multiple choice questions to identify the relevant holding of a cited case.
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When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset.
TL;DR: In this article, the authors present a new dataset called Case Holdings On Legal Decisions (CaseHOLD), which consists of over 53,000+ multiple choice questions to identify the relevant holding of a cited case.
Proceedings ArticleDOI
Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery
TL;DR: Temporal cluster matching (TCM) as discussed by the authors detects building changes in time series of remotely sensed imagery when footprint labels are observed only once, based on the relationship between spectral values inside and outside of building's footprint.
Journal ArticleDOI
Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection
Peter Mark Henderson,Ben Chugg,Brandon R. Anderson,Kristen M. Altenburger,Alexander Harrison Turk,John Guyton,Jacob Goldin,Daniel E. Ho +7 more
TL;DR: This work provides a novel mechanism for unbiased population estimation that achieves rewards comparable to baseline approaches and has the potential to improve audit performance, while maintaining policy-relevant estimates of the tax gap.
Journal ArticleDOI
Enhancing environmental enforcement with near real-time monitoring: Likelihood-based detection of structural expansion of intensive livestock farms
TL;DR: In this paper, the authors demonstrate a process for rapid identification of significant structural expansion using Planet's 3m/pixel satellite imagery products and focusing on Concentrated Animal Feeding Operations (CAFOs) in the US.