21 citations · 54 across the 19 of their papers we have counts for
22 papers
Strategic Multi-Agent Resource Allocation via Statistical Contracts
Ben Chugg, Michael I. Jordan, Aaditya Ramdas
We study a class of strategic hypothesis testing problems in which a principal aims to allocate resources among competing agents who possess data or data-generating mechanisms that…
Bentkus-type asymptotic e-values
Diego Martinez-Taboada, Ben Chugg, Aaditya Ramdas
Asymptotic e-values are emerging as a powerful alternative to asymptotic p-values, particularly in post-hoc inference and multiple testing, where significance levels may be data-de…
Post-Hoc Large-Sample Statistical Inference
Ben Chugg, Etienne Gauthier, Michael I. Jordan +2
We derive inferential procedures for large sample sizes that remain valid under data-dependent significance levels (so-called "post-hoc valid inference"). Classical statistical too…
E-values as statistical evidence: A comparison to Bayes factors, likelihoods, and p-values
Ben Chugg, Aaditya Ramdas, Peter Grünwald
A recurring debate in the philosophy of statistics concerns what, exactly, should count as a measure of evidence for or against a given hypothesis. P-values, likelihood ratios, and…
Closed-form empirical Bernstein confidence sequences for scalars and matrices
Ben Chugg, Aaditya Ramdas
We derive a new closed-form variance-adaptive confidence sequence (CS) for estimating the average conditional mean of a sequence of bounded random variables. Empirically, it yields…
Rao-Blackwellized e-variables
Dante de Roos, Ben Chugg, Peter Grünwald +1
We show that for any concave utility, the expected utility of an e-variable can only increase after conditioning on a sufficient statistic, echoing the classic Rao-Blackwell theore…
A variational approach to dimension-free self-normalized concentration
Ben Chugg, Aaditya Ramdas
We study the self-normalized concentration of vector-valued stochastic processes. We focus on bounds for "sub-" processes, a well-known and quite general class that encompasses…
On admissibility in post-hoc hypothesis testing
Ben Chugg, Tyron Lardy, Aaditya Ramdas +1
The validity of classical hypothesis testing requires the significance level be fixed before any statistical analysis takes place. This is a stringent requirement. For instance…
Nonasymptotic Heavy-Tailed Mean Estimation in Smooth Banach Spaces
Justin Whitehouse, Ben Chugg, Diego Martinez-Taboada +1
We consider the problem of nonasymptotic mean estimation for heavy-tailed random variables taking values in a smooth Banach space. In particular, we extend and improve a simple tru…
Time-Uniform Confidence Spheres for Means of Random Vectors
Ben Chugg, Hongjian Wang, Aaditya Ramdas
We study sequential mean estimation in . In particular, we derive time-uniform confidence spheres -- confidence sphere sequences (CSSs) -- which contain the mean of r…
Auditing Fairness by Betting
Ben Chugg, Santiago Cortes-Gomez, Bryan Wilder +1
We provide practical, efficient, and nonparametric methods for auditing the fairness of deployed classification and regression models. Whereas previous work relies on a fixed-sampl…
A unified recipe for deriving (time-uniform) PAC-Bayes bounds
Ben Chugg, Hongjian Wang, Aaditya Ramdas
We present a unified framework for deriving PAC-Bayesian generalization bounds. Unlike most previous literature on this topic, our bounds are anytime-valid (i.e., time-uniform), me…
Entropy Regularization for Population Estimation
Ben Chugg, Peter Henderson, Jacob Goldin +1
Entropy regularization is known to improve exploration in sequential decision-making problems. We show that this same mechanism can also lead to nearly unbiased and lower-variance…
Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application under the Clean Water Act
Ben Chugg, Nicolas Rothbacher, Alex Feng +2
This paper introduces a new, highly consequential setting for the use of computer vision for environmental sustainability. Concentrated Animal Feeding Operations (CAFOs) (aka inten…
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…
Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits
Ben Chugg, Daniel E. Ho
In many public health settings, there is a perceived tension between allocating resources to known vulnerable areas and learning about the overall prevalence of the problem. Inspir…
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…
Simultaneous Visibility Representations of Undirected Pairs of Graphs
Ben Chugg, William S. Evans, Kelvin Wong
We consider the problem of determining if a pair of undirected graphs , which share the same vertex set, has a representation using opaq…
Output-Oblivious Stochastic Chemical Reaction Networks
Ben Chugg, Anne Condon, Hooman Hashemi
We classify the functions which are stably computable by output-oblivious Stochastic Chemical Reaction Networks (CRNs), i.e., systems of rea…
Submodular Stochastic Probing with Prices
Ben Chugg, Takanori Maehara
We introduce Stochastic Probing with Prices (SPP), a variant of the Stochastic Probing (SP) model in which we must pay a price to probe an element. A SPP problem involves two set s…