1 citations · 1 across the 3 of their papers we have counts for
8 papers
Pre-integration via Active Subspaces
Sifan Liu, Art B. Owen
Pre-integration is an extension of conditional Monte Carlo to quasi-Monte Carlo and randomized quasi-Monte Carlo. It can reduce but not increase the variance in Monte Carlo. For qu…
Quasi-Newton Quasi-Monte Carlo for variational Bayes
Sifan Liu, Art B. Owen
Many machine learning problems optimize an objective that must be measured with noise. The primary method is a first order stochastic gradient descent using one or more Monte Carlo…
How to reduce dimension with PCA and random projections?
Fan Yang, Sifan Liu, Edgar Dobriban +1
In our "big data" age, the size and complexity of data is steadily increasing. Methods for dimension reduction are ever more popular and useful. Two distinct types of dimension red…
Optimal Iterative Sketching with the Subsampled Randomized Hadamard Transform
Jonathan Lacotte, Sifan Liu, Edgar Dobriban +1
Random projections or sketching are widely used in many algorithmic and learning contexts. Here we study the performance of iterative Hessian sketch for least-squares problems. By…
p-Value as the Strength of Evidence Measured by Confidence Distribution
Sifan Liu, Regina Liu, Min-ge Xie
The notion of p-value is a fundamental concept in statistical inference and has been widely used for reporting outcomes of hypothesis tests. However, p-value is often misinterprete…
Ridge Regression: Structure, Cross-Validation, and Sketching
Sifan Liu, Edgar Dobriban
We study the following three fundamental problems about ridge regression: (1) what is the structure of the estimator? (2) how to correctly use cross-validation to choose the regula…