21 citations · 53 across the 6 of their papers we have counts for
6 papers
Integrating Reward Maximization and Population Estimation: Sequential Decision-Making for Internal Revenue Service Audit Selection
Peter Henderson, Ben Chugg, Brandon Anderson +5
We introduce a new setting, optimize-and-estimate structured bandits. Here, a policy must select a batch of arms, each characterized by its own context, that would allow it to both…
Mapping industrial poultry operations at scale with deep learning and aerial imagery
Caleb Robinson, Ben Chugg, Brandon Anderson +2
Concentrated Animal Feeding Operations (CAFOs) pose serious risks to air, water, and public health, but have proven to be challenging to regulate. The U.S. Government Accountabilit…
Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy
Peter Henderson, Ben Chugg, Brandon Anderson +1
We explore the promises and challenges of employing sequential decision-making algorithms -- such as bandits, reinforcement learning, and active learning -- in law and public polic…
Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms
Ben Chugg, Brandon Anderson, Seiji Eicher +2
Much environmental enforcement in the United States has historically relied on either self-reported data or physical, resource-intensive, infrequent inspections. Advances in remote…
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
Lucia Zheng, Neel Guha, Brandon R. Anderson +2
While self-supervised learning has made rapid advances in natural language processing, it remains unclear when researchers should engage in resource-intensive domain-specific pretr…
Temporal Cluster Matching for Change Detection of Structures from Satellite Imagery
Caleb Robinson, Anthony Ortiz, Juan M. Lavista Ferres +2
Longitudinal studies are vital to understanding dynamic changes of the planet, but labels (e.g., buildings, facilities, roads) are often available only for a single point in time.…