no code implementations • ICML 2018 • Yuancheng Zhu, John Lafferty
In an intermediate regime, the statistical risk depends on both the sample size and the communication budget.
no code implementations • 13 Feb 2018 • 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.
no code implementations • NeurIPS 2016 • Yuancheng Zhu, Sabyasachi Chatterjee, John Duchi, John Lafferty
The bounds are expressed in terms of a localized and computational analogue of the modulus of continuity that is central to statistical minimax analysis.
no code implementations • 12 Nov 2015 • Yuancheng Zhu, Zhe Liu, Siqi Sun
We present a framework for incorporating prior information into nonparametric estimation of graphical models.
no code implementations • 25 Mar 2015 • 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 ellipsoids.
no code implementations • NeurIPS 2014 • 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.