Fetching the paper…
Reading the bibliography…
When the data are stored in a distributed manner, direct application of traditional statistical inference procedures is often prohibitive due to communication cost and privacy concerns.
Ridge regression: Biased estimation for nonorthogonal problems
Hoerl, A. E · 1970
Earlier work this paper cites.
One-step Huber estimates in the linear model
Bickel, P. J · 1975
Earlier work this paper cites.
Monotone operators and the proximal point algorithm
Rockafellar, R. T · 1976
Earlier work this paper cites.
A method for solving the convex programming problem with convergence rate O ( 1 / k 2 ) O(1/k^{2})
Nesterov, Y. E · 1983
Earlier work this paper cites.
The stochastic difference between econometric statistics
Robinson, P. M · 1988
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
Tibshirani, R · 1996
Earlier work this paper cites.
Variable selection via nonconcave penalized likelihood and its oracle properties
Fan, J · 2001
Earlier work this paper cites.
Numerical optimization (Second Edition)
Nocedal, J · 2006
Earlier work this paper cites.
Nearly unbiased variable selection under minimax concave penalty
Zhang, C.-H · 2010
Earlier work this paper cites.
Distributed optimization and statistical learning via the alternating direction method of multipliers
Boyd, S · 2011
Earlier work this paper cites.
A tail inequality for quadratic forms of subgaussian random vectors
Hsu, D · 2012
Earlier work this paper cites.
Introductory lectures on convex optimization: A basic course
Nesterov, Y · 2013
Earlier work this paper cites.
Parallelizing MCMC via Weierstrass sampler
Wang, X · 2013
Cited alongside, same era.
Communication-efficient algorithms for statistical optimization
Zhang, Y · 2013
Cited alongside, same era.
A split-and-conquer approach for analysis of extraordinarily large data
Chen, X · 2014
Cited alongside, same era.
A scalable bootstrap for massive data
Kleiner, A · 2014
Cited alongside, same era.
Proximal algorithms
Parikh, N · 2014
Cited alongside, same era.
Communication-efficient distributed optimization using an approximate Newton-type method
Shamir, O · 2014
Cited alongside, same era.
Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Xiao, H · 2017
Later among the works it cites.
Distributed testing and estimation under sparse high dimensional models
Battey, H · 2018
Later among the works it cites.
Distributed statistical estimation of high-dimensional and nonparametric distributions
Han, Y · 2018
Later among the works it cites.
A massive data framework for M-estimators with cubic-rate
Shi, C · 2018
Later among the works it cites.
Giant: Globally improved approximate newton method for distributed optimization
Wang, S · 2018
Later among the works it cites.
Divide and conquer in nonstandard problems and the super-efficiency phenomenon
Banerjee, M · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Communication complexity of distributed convex learning and optimization
Arjevani, Y · 2015
Cited alongside, same era.
Disco: Distributed optimization for self-concordant empirical loss
Zhang, Y · 2015
Cited alongside, same era.
On the optimality of averaging in distributed statistical learning
Rosenblatt, J. D · 2016
Cited alongside, same era.
Communication-efficient algorithms for distributed stochastic principal component analysis
Garber, D · 2017
Cited alongside, same era.
Concentration inequalities and moment bounds for sample covariance operators
Koltchinskii, V · 2017
Cited alongside, same era.
Computational limits of a distributed algorithm for smoothing spline
Shang, Z · 2017
Cited alongside, same era.
Closest in time.
Dingo: Distributed Newton-type method for gradient-norm optimization
Crane, R · 2019
Closest in time.
Distributed estimation of principal eigenspaces
Fan, J · 2019
Closest in time.
Communication-efficient distributed statistical inference
Jordan, M. I · 2019
Closest in time.
An asymptotic analysis of distributed nonparametric methods
Szabó, B · 2019
Closest in time.
Distributed inference for quantile regression processes
Volgushev, S · 2019
Closest in time.
First-order Newton-type estimator for distributed estimation and inference
Chen, X · 2021
Closest in time.