6 papers
proxymate: Diagnosis and Adjustment of Proxy Estimates for Reliable Inference
Alexandra N. M. Darmon, Deeksha Sinha, Steve Wilkins-Reeves +1
Proxy outcomes (such as short-term behavioral signals, model predictions, or surrogate endpoints) are frequently used in place of primary outcomes that are too slow to mature, rare…
Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts
Steven Wilkins-Reeves, Alexandra N. M. Darmon, Deeksha Sinha
In many scientific domains, including experimentation, researchers rely on measurements of proxy outcomes to achieve faster and more frequent reads, especially when the primary out…
Transfer Learning Through Conditional Quantile Matching
Yikun Zhang, Steven Wilkins-Reeves, Wesley Lee +1
We introduce a transfer learning framework for regression that leverages heterogeneous source domains to improve predictive performance in a data-scarce target domain. Our approach…
Asymptotically Normal Estimation of Local Latent Network Curvature
Steven Wilkins-Reeves, Tyler McCormick
Network data, commonly used throughout the physical, social, and biological sciences, consist of nodes (individuals) and the edges (interactions) between them. One way to represent…
Model-Based Inference and Experimental Design for Interference Using Partial Network Data
Steven Wilkins Reeves, Shane Lubold, Arun G. Chandrasekhar +1
The stable unit treatment value assumption states that the outcome of an individual is not affected by the treatment statuses of others, however in many real world applications, tr…
Multiply Robust Estimation for Local Distribution Shifts with Multiple Domains
Steven Wilkins-Reeves, Xu Chen, Qi Ma +2
Distribution shifts are ubiquitous in real-world machine learning applications, posing a challenge to the generalization of models trained on one data distribution to another. We f…