24 citations · 52 across the 13 of their papers we have counts for
13 papers
An Exact Sampler for Inference after Polyhedral Model Selection
Sifan Liu
Inference after model selection presents computational challenges when dealing with intractable conditional distributions. Markov chain Monte Carlo (MCMC) is a common method for sa…
Selective Inference with Distributed Data
Sifan Liu, Snigdha Panigrahi
As datasets grow larger, they are often distributed across multiple machines that compute in parallel and communicate with a central machine through short messages. In this paper,…
Conditional Quasi-Monte Carlo with Constrained Active Subspaces
Sifan Liu
Conditional Monte Carlo or pre-integration is a powerful tool for reducing variance and improving the regularity of integrands when using Monte Carlo and quasi-Monte Carlo (QMC) me…
Black-box Selective Inference via Bootstrapping
Sifan Liu, Jelena Markovic-Voronov, Jonathan Taylor
Conditional selective inference requires an exact characterization of the selection event, which is often unavailable except for a few examples like the lasso. This work addresses…
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…
Global and Individualized Community Detection in Inhomogeneous Multilayer Networks
Shuxiao Chen, Sifan Liu, Zongming Ma
In network applications, it has become increasingly common to obtain datasets in the form of multiple networks observed on the same set of subjects, where each network is obtained…
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…
Asymptotics for Sketching in Least Squares Regression
Edgar Dobriban, Sifan Liu
We consider a least squares regression problem where the data has been generated from a linear model, and we are interested to learn the unknown regression parameters. We consider…
Error Detection in a Large-Scale Lexical Taxonomy
Sifan Liu, Hongzhi Wang
Knowledge base (KB) is an important aspect in artificial intelligence. One significant challenge faced by KB construction is that it contains many noises, which prevents its effect…