7 papers
Blackwell Approachability and Gradient Equilibrium are Equivalent
Brian W. Lee, Nika Haghtalab, Michael I. Jordan +1
Gradient equilibrium (GEQ) is a recently introduced online optimization framework that generalizes first-order stationarity from offline optimization and abstracts problems like on…
Unifying Different Theories of Conformal Prediction
Rina Foygel Barber, Ryan J. Tibshirani
This paper presents a unified framework for understanding the methodology and theory behind several different methods in the conformal prediction literature, which includes standar…
Gradient Equilibrium in Online Learning: Theory and Applications
Anastasios N. Angelopoulos, Michael I. Jordan, Ryan J. Tibshirani
We present a new perspective on online learning that we refer to as gradient equilibrium: a sequence of iterates achieves gradient equilibrium if the average of gradients of losses…
Revisiting Optimism and Model Complexity in the Wake of Overparameterized Machine Learning
Pratik Patil, Jin-Hong Du, Ryan J. Tibshirani
Common practice in modern machine learning involves fitting a large number of parameters relative to the number of observations. These overparameterized models can exhibit surprisi…
Unbiased Test Error Estimation in the Poisson Means Problem via Coupled Bootstrap Techniques
Natalia L. Oliveira, Jing Lei, Ryan J. Tibshirani
We propose a coupled bootstrap (CB) method for the test error of an arbitrary algorithm that estimates the mean in a Poisson sequence, often called the Poisson means problem. The i…
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…