Timezone: »
Poster
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 an extension to Pinsker's theorem where estimation is carried out under storage or communication constraints. In particular, we place limits on the number of bits used to encode an estimator, and analyze the excess risk in terms of this constraint, the signal size, and the noise level. We give sharp upper and lower bounds for the case of a Euclidean ball, which establishes the Pareto-optimal minimax tradeoff between storage and risk in this setting.
Author Information
Yuancheng Zhu (University of Chicago)
John Lafferty (Yale University)
More from the Same Authors
-
2016 Workshop: Adaptive and Scalable Nonparametric Methods in Machine Learning »
Aaditya Ramdas · Arthur Gretton · Bharath Sriperumbudur · Han Liu · John Lafferty · Samory Kpotufe · Zoltán Szabó -
2016 Poster: Local Minimax Complexity of Stochastic Convex Optimization »
sabyasachi chatterjee · John Duchi · John Lafferty · Yuancheng Zhu -
2016 Poster: Selective inference for group-sparse linear models »
Fan Yang · Rina Barber · Prateek Jain · John Lafferty -
2015 Poster: A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements »
Qinqing Zheng · John Lafferty -
2014 Poster: Blossom Tree Graphical Models »
Zhe Liu · John Lafferty -
2013 Workshop: Modern Nonparametric Methods in Machine Learning »
Arthur Gretton · Mladen Kolar · Samory Kpotufe · John Lafferty · Han Liu · Bernhard Schölkopf · Alexander Smola · Rob Nowak · Mikhail Belkin · Lorenzo Rosasco · peter bickel · Yue Zhao -
2012 Workshop: Modern Nonparametric Methods in Machine Learning »
Sivaraman Balakrishnan · Arthur Gretton · Mladen Kolar · John Lafferty · Han Liu · Tong Zhang -
2012 Poster: Nonparametric Reduced Rank Regression »
Rina Foygel · Michael Horrell · Mathias Drton · John Lafferty -
2012 Poster: Exponential Concentration for Mutual Information Estimation with Application to Forests »
Han Liu · John Lafferty · Larry Wasserman -
2011 Workshop: Copulas in Machine Learning »
Gal Elidan · Zoubin Ghahramani · John Lafferty -
2010 Spotlight: Graph-Valued Regression »
Han Liu · Xi Chen · John Lafferty · Larry Wasserman -
2010 Poster: Graph-Valued Regression »
Han Liu · Xi Chen · John Lafferty · Larry Wasserman -
2008 Poster: Nonparametric regression and classification with joint sparsity constraints »
Han Liu · John Lafferty · Larry Wasserman -
2008 Spotlight: Nonparametric regression and classification with joint sparsity constraints »
Han Liu · John Lafferty · Larry Wasserman -
2007 Poster: SpAM: Sparse Additive Models »
Pradeep Ravikumar · Han Liu · John Lafferty · Larry Wasserman -
2007 Spotlight: SpAM: Sparse Additive Models »
Pradeep Ravikumar · Han Liu · John Lafferty · Larry Wasserman -
2007 Spotlight: Statistical Analysis of Semi-Supervised Regression »
John Lafferty · Larry Wasserman -
2007 Poster: Statistical Analysis of Semi-Supervised Regression »
John Lafferty · Larry Wasserman -
2007 Poster: Compressed Regression »
Shuheng Zhou · John Lafferty · Larry Wasserman -
2006 Poster: Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood »
Martin J Wainwright · Pradeep Ravikumar · John Lafferty -
2006 Spotlight: Inferring Graphical Model Structure using $\ell_1$-Regularized Pseudo-Likelihood »
Martin J Wainwright · Pradeep Ravikumar · John Lafferty