565 citations · 2.9k across the 63 of their papers we have counts for
72 papers
Conformal Prediction Through the Lens of Hypothesis Testing: Universality, Impossibility, and Optimality
Ryan J. Tibshirani, Rina Foygel Barber, Aaditya Ramdas
The connections between conformal prediction and permutation tests are already widely-known in the literature. Some authors motivate conformal prediction by saying that it computes…
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…
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…