21 citations · 43 across the 13 of their papers we have counts for
4 papers · 1 filter
Dimensionality Reduction for General KDE Mode Finding
Xinyu Luo, Christopher Musco, Cas Widdershoven
Finding the mode of a high dimensional probability distribution is a fundamental algorithmic problem in statistics and data analysis. There has been particular interest in effi…
The Statistical Cost of Robust Kernel Hyperparameter Tuning
Raphael A. Meyer, Christopher Musco
This paper studies the statistical complexity of kernel hyperparameter tuning in the setting of active regression under adversarial noise. We consider the problem of finding the be…
Graph Learning for Inverse Landscape Genetics
Prathamesh Dharangutte, Christopher Musco
The problem of inferring unknown graph edges from numerical data at a graph's nodes appears in many forms across machine learning. We study a version of this problem that arises in…
Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
Haim Avron, Michael Kapralov, Cameron Musco +3
Random Fourier features is one of the most popular techniques for scaling up kernel methods, such as kernel ridge regression. However, despite impressive empirical results, the sta…