4 citations · 5 across the 17 of their papers we have counts for
7 papers · 1 filter
Conformalized Percentile Interval: Finite Sample Validity and Improved Conditional Performance
Ran Zou, Wanrong Zhu, Bin Nan
Conformal prediction provides distribution-free predictive intervals with finite-sample marginal coverage. However, achieving conditional validity and interval efficiency (in terms…
Refining Covariance Matrix Estimation in Stochastic Gradient Descent Through Bias Reduction
Ziyang Wei, Wanrong Zhu, Jingyang Lyu +1
We study online inference and asymptotic covariance estimation for the stochastic gradient descent (SGD) algorithm. While classical methods (such as plug-in and batch-means estimat…
A Flexible Empirical Bayes Approach to Generalized Linear Models, with Applications to Sparse Logistic Regression
Dongyue Xie, Matthew Stephens
We introduce a flexible empirical Bayes approach for fitting Bayesian generalized linear models. Specifically, we adopt a novel mean-field variational inference (VI) method and the…
Online Statistical Inference of Constrained Stochastic Optimization via Random Scaling
Xinchen Du, Wanrong Zhu, Wei Biao Wu +1
Constrained stochastic nonlinear optimization problems have attracted significant attention for their ability to model complex real-world scenarios in physics, economics, and biolo…
High Confidence Level Inference is Almost Free using Parallel Stochastic Optimization
Wanrong Zhu, Zhipeng Lou, Ziyang Wei +1
Uncertainty quantification for estimation through stochastic optimization solutions in an online setting has gained popularity recently. This paper introduces a novel inference met…
Weighted Averaged Stochastic Gradient Descent: Asymptotic Normality and Optimality
Ziyang Wei, Wanrong Zhu, Wei Biao Wu
Stochastic Gradient Descent (SGD) is one of the most popular algorithms in statistical and machine learning due to its computational and memory efficiency. Various averaging scheme…
Online Covariance Matrix Estimation in Stochastic Gradient Descent
Wanrong Zhu, Xi Chen, Wei Biao Wu
The stochastic gradient descent (SGD) algorithm is widely used for parameter estimation, especially for huge data sets and online learning. While this recursive algorithm is popula…