565 citations · 2.9k across the 63 of their papers we have counts for
14 papers · 1 filter
Reliable AI-Assisted Decisions via Selective Auditing
Isaac Gibbs, Elise L. Han, Ryan J. Tibshirani +1
We consider online monitoring of an AI system that produces a sequence of decisions. At each round, the system outputs a proposed decision together with a confidence score estimati…
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…
Class-Conditional Conformal Prediction with Many Classes
Tiffany Ding, Anastasios N. Angelopoulos, Stephen Bates +2
Standard conformal prediction methods provide a marginal coverage guarantee, which means that for a random test point, the conformal prediction set contains the true label with a u…
The Voronoigram: Minimax Estimation of Bounded Variation Functions From Scattered Data
Addison J. Hu, Alden Green, Ryan J. Tibshirani
We consider the problem of estimating a multivariate function of bounded variation (BV), from noisy observations made at random design points $x_i \in…
Multivariate Trend Filtering for Lattice Data
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison J. Hu +1
We study a multivariate version of trend filtering, called Kronecker trend filtering or KTF, for the case in which the design points form a lattice in dimensions. KTF is a natu…
Flexible Model Aggregation for Quantile Regression
Rasool Fakoor, Taesup Kim, Jonas Mueller +2
Quantile regression is a fundamental problem in statistical learning motivated by a need to quantify uncertainty in predictions, or to model a diverse population without being over…
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani
We study the implicit regularization of mini-batch stochastic gradient descent, when applied to the fundamental problem of least squares regression. We leverage a continuous-time s…
A Higher-Order Kolmogorov-Smirnov Test
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas +1
We present an extension of the Kolmogorov-Smirnov (KS) two-sample test, which can be more sensitive to differences in the tails. Our test statistic is an integral probability metri…
A Continuous-Time View of Early Stopping for Least Squares
Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
We study the statistical properties of the iterates generated by gradient descent, applied to the fundamental problem of least squares regression. We take a continuous-time view, i…
Additive Models with Trend Filtering
Veeranjaneyulu Sadhanala, Ryan J. Tibshirani
We study additive models built with trend filtering, i.e., additive models whose components are each regularized by the (discrete) total variation of their th (discrete) derivat…
Trend Filtering on Graphs
Yu-Xiang Wang, James Sharpnack, Alex Smola +1
We introduce a family of adaptive estimators on graphs, based on penalizing the norm of discrete graph differences. This generalizes the idea of trend filtering [Kim et al…
A General Framework for Fast Stagewise Algorithms
Ryan J. Tibshirani
Forward stagewise regression follows a very simple strategy for constructing a sequence of sparse regression estimates: it starts with all coefficients equal to zero, and iterative…
Fast and Flexible ADMM Algorithms for Trend Filtering
Aaditya Ramdas, Ryan J. Tibshirani
This paper presents a fast and robust algorithm for trend filtering, a recently developed nonparametric regression tool. It has been shown that, for estimating functions whose deri…
The Falling Factorial Basis and Its Statistical Applications
Yu-Xiang Wang, Alex Smola, Ryan J. Tibshirani
We study a novel spline-like basis, which we name the "falling factorial basis", bearing many similarities to the classic truncated power basis. The advantage of the falling factor…