3 citations · 6 across the 8 of their papers we have counts for
9 papers
FUSE: Ensembling Verifiers with Zero Labeled Data
Joonhyuk Lee, Virginia Ma, Sarah Zhao +4
Verification of model outputs is rapidly emerging as a key primitive for both training and real-world deployment of large language models (LLMs). In practice, this often involves u…
Chiseling: Powerful and Valid Subgroup Selection via Interactive Machine Learning
Nathan Cheng, Asher Spector, Lucas Janson
In regression and causal inference, controlled subgroup selection aims to identify, with inferential guarantees, a subgroup (defined as a subset of the covariate space) on which th…
Mosaic inference on panel data
Asher Spector, Rina Foygel Barber, Emmanuel Candès
Analysis of panel data via linear regression is widespread across disciplines. To perform statistical inference, such analyses typically assume that clusters of observations are jo…
Using Individualized Treatment Effects to Assess Treatment Effect Heterogeneity
Konstantinos Sechidis, Cong Zhang, Sophie Sun +3
Assessing treatment effect heterogeneity (TEH) in clinical trials is crucial, as it provides insights into the variability of treatment responses among patients, influencing import…
The mosaic permutation test: an exact and nonparametric goodness-of-fit test for factor models
Asher Spector, Rina Foygel Barber, Trevor Hastie +2
Financial firms often rely on fundamental factor models to explain correlations among asset returns and manage risk. Yet after major events, e.g., COVID-19, analysts may reassess w…
Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects
Wenlong Ji, Lihua Lei, Asher Spector
Many causal estimands are only partially identifiable since they depend on the unobservable joint distribution between potential outcomes. Stratification on pretreatment covariates…
Asymptotically Optimal Knockoff Statistics via the Masked Likelihood Ratio
Asher Spector, William Fithian
In feature selection problems, knockoffs are synthetic controls for the original features. Employing knockoffs allows analysts to use nearly any variable importance measure or "fea…
Controlled Discovery and Localization of Signals via Bayesian Linear Programming
Asher Spector, Lucas Janson
Scientists often must simultaneously localize and discover signals. For instance, in genetic fine-mapping, high correlations between nearby genetic variants make it hard to identif…
Powerful Knockoffs via Minimizing Reconstructability
Asher Spector, Lucas Janson
Model-X knockoffs allows analysts to perform feature selection using almost any machine learning algorithm while still provably controlling the expected proportion of false discove…