
Convergence and Alignment of Gradient Descent with Random Back Propagation Weights
Stochastic gradient descent with backpropagation is the workhorse of art...
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The huge Package for Highdimensional Undirected Graph Estimation in R
We describe an R package named huge which provides easytouse functions...
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Model Repair: Robust Recovery of OverParameterized Statistical Models
A new type of robust estimation problem is introduced where the goal is ...
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Surfing: Iterative optimization over incrementally trained deep networks
We investigate a sequential optimization procedure to minimize the empir...
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Fair quantile regression
Quantile regression is a tool for learning conditional distributions. In...
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TopicEq: A Joint Topic and Mathematical Equation Model for Scientific Texts
Scientific documents rely on both mathematics and text to communicate id...
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Prediction Rule Reshaping
Two methods are proposed for highdimensional shapeconstrained regressi...
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Distributed Nonparametric Regression under Communication Constraints
This paper studies the problem of nonparametric estimation of a smooth f...
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Testing for Global Network Structure Using Small Subgraph Statistics
We study the problem of testing for community structure in networks usin...
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Local Minimax Complexity of Stochastic Convex Optimization
We extend the traditional worstcase, minimax analysis of stochastic con...
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Convergence Analysis for Rectangular Matrix Completion Using BurerMonteiro Factorization and Gradient Descent
We address the rectangular matrix completion problem by lifting the unkn...
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A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
We propose a simple, scalable, and fast gradient descent algorithm to op...
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Quantized Nonparametric Estimation over Sobolev Ellipsoids
We formulate the notion of minimax estimation under storage or communica...
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Faithful Variable Screening for HighDimensional Convex Regression
We study the problem of variable selection in convex nonparametric regre...
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Quantized Estimation of Gaussian Sequence Models in Euclidean Balls
A central result in statistical theory is Pinsker's theorem, which chara...
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Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk
We present an iterative Markov chainMonte Carlo algorithm for computingr...
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Nonparametric Reduced Rank Regression
We propose an approach to multivariate nonparametric regression that gen...
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ExpectationPropogation for the Generative Aspect Model
The generative aspect model is an extension of the multinomial model for...
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Variational Chernoff Bounds for Graphical Models
Recent research has made significant progress on the problem of bounding...
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The Nonparanormal SKEPTIC
We propose a semiparametric approach, named nonparanormal skeptic, for e...
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Conditional Sparse Coding and Grouped Multivariate Regression
We study the problem of multivariate regression where the data are natur...
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Sparse Additive Functional and Kernel CCA
Canonical Correlation Analysis (CCA) is a classical tool for finding cor...
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High Dimensional Semiparametric Gaussian Copula Graphical Models
In this paper, we propose a semiparametric approach, named nonparanormal...
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Sparse Nonparametric Graphical Models
We present some nonparametric methods for graphical modeling. In the dis...
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Union Support Recovery in Multitask Learning
We sharply characterize the performance of different penalization scheme...
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Forest Density Estimation
We study graph estimation and density estimation in high dimensions, usi...
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Time Varying Undirected Graphs
Undirected graphs are often used to describe high dimensional distributi...
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Compressed Regression
Recent research has studied the role of sparsity in high dimensional reg...
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