29 citations · 57 across the 8 of their papers we have counts for
8 papers
Estimation and inference for minimizer and minimum of convex functions: optimality, adaptivity and uncertainty principles
T. Tony Cai, Ran Chen, Yuancheng Zhu
Optimal estimation and inference for both the minimizer and minimum of a convex regression function under the white noise and nonparametric regression models are studied in a nonas…
Distributed Nonparametric Regression under Communication Constraints
Yuancheng Zhu, John Lafferty
This paper studies the problem of nonparametric estimation of a smooth function with data distributed across multiple machines. We assume an independent sample from a white noise m…
HiGrad: Uncertainty Quantification for Online Learning and Stochastic Approximation
Weijie J. Su, Yuancheng Zhu
Stochastic gradient descent (SGD) is an immensely popular approach for online learning in settings where data arrives in a stream or data sizes are very large. However, despite an…
Local Minimax Complexity of Stochastic Convex Optimization
Yuancheng Zhu, Sabyasachi Chatterjee, John Duchi +1
We extend the traditional worst-case, minimax analysis of stochastic convex optimization by introducing a localized form of minimax complexity for individual functions. Our main re…
Learning Nonparametric Forest Graphical Models with Prior Information
Yuancheng Zhu, Zhe Liu, Siqi Sun
We present a framework for incorporating prior information into nonparametric estimation of graphical models. To avoid distributional assumptions, we restrict the graph to be a for…
Quantized Nonparametric Estimation over Sobolev Ellipsoids
Yuancheng Zhu, John Lafferty
We formulate the notion of minimax estimation under storage or communication constraints, and prove an extension to Pinsker's theorem for nonparametric estimation over Sobolev elli…
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls
Yuancheng Zhu, John Lafferty
A central result in statistical theory is Pinsker's theorem, which characterizes the minimax rate in the normal means model of nonparametric estimation. In this paper, we present a…
The log-shift penalty for adaptive estimation of multiple Gaussian graphical models
Yuancheng Zhu, Rina Foygel Barber
Sparse Gaussian graphical models characterize sparse dependence relationships between random variables in a network. To estimate multiple related Gaussian graphical models on the s…