148 citations · 790 across the 56 of their papers we have counts for
8 papers · 1 filter
Randomized Nyström Preconditioning
Zachary Frangella, Joel A. Tropp, Madeleine Udell
This paper introduces the Nyström PCG algorithm for solving a symmetric positive-definite linear system. The algorithm applies the randomized Nyström method to form a low-rank appr…
Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression
William T. Stephenson, Zachary Frangella, Madeleine Udell +1
Models like LASSO and ridge regression are extensively used in practice due to their interpretability, ease of use, and strong theoretical guarantees. Cross-validation (CV) is wide…
ControlBurn: Feature Selection by Sparse Forests
Brian Liu, Miaolan Xie, Madeleine Udell
Tree ensembles distribute feature importance evenly amongst groups of correlated features. The average feature ranking of the correlated group is suppressed, which reduces interpre…
Privileged Zero-Shot AutoML
Nikhil Singh, Brandon Kates, Jeff Mentch +3
This work improves the quality of automated machine learning (AutoML) systems by using dataset and function descriptions while significantly decreasing computation time from minute…
How Low Can We Go: Trading Memory for Error in Low-Precision Training
Chengrun Yang, Ziyang Wu, Jerry Chee +2
Low-precision arithmetic trains deep learning models using less energy, less memory and less time. However, we pay a price for the savings: lower precision may yield larger round-o…
An automatic system to detect equivalence between iterative algorithms
Shipu Zhao, Laurent Lessard, Madeleine Udell
When are two algorithms the same? How can we be sure a recently proposed algorithm is novel, and not a minor twist on an existing method? In this paper, we present a framework for…
Tensor Random Projection for Low Memory Dimension Reduction
Yiming Sun, Yang Guo, Joel A. Tropp +1
Random projections reduce the dimension of a set of vectors while preserving structural information, such as distances between vectors in the set. This paper proposes a novel use o…
TenIPS: Inverse Propensity Sampling for Tensor Completion
Chengrun Yang, Lijun Ding, Ziyang Wu +1
Tensors are widely used to represent multiway arrays of data. The recovery of missing entries in a tensor has been extensively studied, generally under the assumption that entries…