Search Results for author:
Found 7 papers, 3 papers with code
Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
In this paper, we propose a generic semiparametric inference framework for doubly robust estimation with multiple derived outcomes, which also encompasses the usual setting of multiple outcomes when the response of each unit is available.
Optimal Ridge Regularization for Out-of-Distribution Prediction
We study the behavior of optimal ridge regularization and optimal ridge risk for out-of-distribution prediction, where the test distribution deviates arbitrarily from the train distribution.
Corrected generalized cross-validation for finite ensembles of penalized estimators
We provide a non-asymptotic analysis of the CGCV and the two intermediate risk estimators for ensembles of convex penalized estimators under Gaussian features and a linear response model.
Simultaneous inference for generalized linear models with unmeasured confounders
Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes.
Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation
We study subsampling-based ridge ensembles in the proportional asymptotics regime, where the feature size grows proportionally with the sample size such that their ratio converges to a constant.
Extrapolated cross-validation for randomized ensembles
By establishing uniform consistency of our risk extrapolation technique over ensemble and subsample sizes, we show that ECV yields $\delta$-optimal (with respect to the oracle-tuned risk) ensembles for squared prediction risk.
Bagging in overparameterized learning: Risk characterization and risk monotonization
Bagging is a commonly used ensemble technique in statistics and machine learning to improve the performance of prediction procedures.
