24 citations · 59 across the 10 of their papers we have counts for
10 papers
Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method
Ryan J. Tibshirani, Samy Wu Fung, Howard Heaton +1
We study approximations to the Moreau envelope -- and infimal convolutions more broadly -- based on Laplace's method, a classical tool in analysis which ties certain integrals to s…
Optimal Ridge Regularization for Out-of-Distribution Prediction
Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani
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 dis…
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent
Pratik Patil, Yuchen Wu, Ryan J. Tibshirani
We analyze the statistical properties of generalized cross-validation (GCV) and leave-one-out cross-validation (LOOCV) applied to early-stopped gradient descent (GD) in high-dimens…
Conformal PID Control for Time Series Prediction
Anastasios N. Angelopoulos, Emmanuel J. Candes, Ryan J. Tibshirani
We study the problem of uncertainty quantification for time series prediction, with the goal of providing easy-to-use algorithms with formal guarantees. The algorithms we present b…
Excess Optimism: How Biased is the Apparent Error of an Estimator Tuned by SURE?
Ryan J. Tibshirani, Saharon Rosset
Nearly all estimators in statistical prediction come with an associated tuning parameter, in one way or another. Common practice, given data, is to choose the tuning parameter valu…
The Multiple Quantile Graphical Model
Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
We introduce the Multiple Quantile Graphical Model (MQGM), which extends the neighborhood selection approach of Meinshausen and Buhlmann for learning sparse graphical models. The l…