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Correcting the bias in least squares regression with volume-rescaled
  sampling

Correcting the bias in least squares regression with volume-rescaled sampling

4 October 2018
Michal Derezinski
Manfred K. Warmuth
Daniel J. Hsu
ArXiv (abs)PDFHTML

Papers citing "Correcting the bias in least squares regression with volume-rescaled sampling"

12 / 12 papers shown
Title
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm
FedSSO: A Federated Server-Side Second-Order Optimization Algorithm
Xinteng Ma
Renyi Bao
Jinpeng Jiang
Yang Liu
Arthur Jiang
Junhua Yan
Xin Liu
Zhisong Pan
FedML
87
7
0
20 Jun 2022
Optimal Sublinear Sampling of Spanning Trees and Determinantal Point
  Processes via Average-Case Entropic Independence
Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence
Nima Anari
Yang P. Liu
T. Vuong
54
18
0
06 Apr 2022
Query Complexity of Least Absolute Deviation Regression via Robust
  Uniform Convergence
Query Complexity of Least Absolute Deviation Regression via Robust Uniform Convergence
Xue Chen
Michal Derezinski
61
31
0
03 Feb 2021
Debiasing Distributed Second Order Optimization with Surrogate Sketching
  and Scaled Regularization
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization
Michal Derezinski
Burak Bartan
Mert Pilanci
Michael W. Mahoney
51
27
0
02 Jul 2020
Sampling from a $k$-DPP without looking at all items
Sampling from a kkk-DPP without looking at all items
Daniele Calandriello
Michal Derezinski
Michal Valko
61
24
0
30 Jun 2020
Exact expressions for double descent and implicit regularization via
  surrogate random design
Exact expressions for double descent and implicit regularization via surrogate random design
Michal Derezinski
Feynman T. Liang
Michael W. Mahoney
80
78
0
10 Dec 2019
Bayesian experimental design using regularized determinantal point
  processes
Bayesian experimental design using regularized determinantal point processes
Michal Derezinski
Feynman T. Liang
Michael W. Mahoney
48
26
0
10 Jun 2019
Exact sampling of determinantal point processes with sublinear time
  preprocessing
Exact sampling of determinantal point processes with sublinear time preprocessing
Michal Derezinski
Daniele Calandriello
Michal Valko
95
55
0
31 May 2019
Distributed estimation of the inverse Hessian by determinantal averaging
Distributed estimation of the inverse Hessian by determinantal averaging
Michal Derezinski
Michael W. Mahoney
67
31
0
28 May 2019
Efficient online learning with kernels for adversarial large scale
  problems
Efficient online learning with kernels for adversarial large scale problems
Rémi Jézéquel
Pierre Gaillard
Alessandro Rudi
77
13
0
26 Feb 2019
Minimax experimental design: Bridging the gap between statistical and
  worst-case approaches to least squares regression
Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
Michal Derezinski
K. Clarkson
Michael W. Mahoney
Manfred K. Warmuth
107
25
0
04 Feb 2019
Fast determinantal point processes via distortion-free intermediate
  sampling
Fast determinantal point processes via distortion-free intermediate sampling
Michal Derezinski
86
40
0
08 Nov 2018
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