21 citations · 27 across the 5 of their papers we have counts for
5 papers
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…
High-dimensional Multi-class Classification with Presence-only Data
Lili Zheng, Garvesh Raskutti
Classification with positive and unlabeled (PU) data frequently arises in bioinformatics, clinical data, and ecological studies, where collecting negative samples can be prohibitiv…
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…
Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression
Han Chen, Garvesh Raskutti, Ming Yuan
In this paper, we consider the problem of learning high-dimensional tensor regression problems with low-rank structure. One of the core challenges associated with learning high-dim…
Minimax Optimal Rates for Poisson Inverse Problems with Physical Constraints
Xin Jiang, Garvesh Raskutti, Rebecca Willett
This paper considers fundamental limits for solving sparse inverse problems in the presence of Poisson noise with physical constraints. Such problems arise in a variety of applicat…