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Distributed Nonparametric Regression under Communication Constraints
In an intermediate regime, the statistical risk depends on both the sample size and the communication budget.
Uncertainty Quantification for Online Learning and Stochastic Approximation via Hierarchical Incremental Gradient Descent
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.
Local Minimax Complexity of Stochastic Convex Optimization
The bounds are expressed in terms of a localized and computational analogue of the modulus of continuity that is central to statistical minimax analysis.
Learning Nonparametric Forest Graphical Models with Prior Information
We present a framework for incorporating prior information into nonparametric estimation of graphical models.
Quantized Nonparametric Estimation over Sobolev Ellipsoids
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.
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls
A central result in statistical theory is Pinsker's theorem, which characterizes the minimax rate in the normal means model of nonparametric estimation.
