289 citations · 1.2k across the 72 of their papers we have counts for
28 papers · 1 filter
Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guarantees
Yue Gao, Garvesh Raskutti, Rebecca Willet
This paper introduces a novel, computationally-efficient algorithm for predictive inference (PI) that requires no distributional assumptions on the data and can be computed faster…
Reduced-Order Autodifferentiable Ensemble Kalman Filters
Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett
This paper introduces a computational framework to reconstruct and forecast a partially observed state that evolves according to an unknown or expensive-to-simulate dynamical syste…
Bagging Provides Assumption-free Stability
Jake A. Soloff, Rina Foygel Barber, Rebecca Willett
Bagging is an important technique for stabilizing machine learning models. In this paper, we derive a finite-sample guarantee on the stability of bagging for any model. Our result…
Lazy Estimation of Variable Importance for Large Neural Networks
Yue Gao, Abby Stevens, Rebecca Willet +1
As opaque predictive models increasingly impact many areas of modern life, interest in quantifying the importance of a given input variable for making a specific prediction has gro…
Auto-differentiable Ensemble Kalman Filters
Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett
Data assimilation is concerned with sequentially estimating a temporally-evolving state. This task, which arises in a wide range of scientific and engineering applications, is part…
Pure Exploration in Kernel and Neural Bandits
Yinglun Zhu, Dongruo Zhou, Ruoxi Jiang +3
We study pure exploration in bandits, where the dimension of the feature representation can be much larger than the number of arms. To overcome the curse of dimensionality, we prop…