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Proportional Volume Sampling and Approximation Algorithms for A-Optimal
  Design
v1v2v3v4v5 (latest)

Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design

22 February 2018
Aleksandar Nikolov
Mohit Singh
U. Tantipongpipat
ArXiv (abs)PDFHTML

Papers citing "Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design"

16 / 16 papers shown
Title
Experimental Design for Any $p$-Norm
Experimental Design for Any ppp-Norm
L. Lau
Robert Wang
Hong Zhou
14
1
0
03 May 2023
Determinantal point processes based on orthogonal polynomials for
  sampling minibatches in SGD
Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD
Rémi Bardenet
Subhro Ghosh
Meixia Lin
75
6
0
11 Dec 2021
High-Dimensional Experimental Design and Kernel Bandits
High-Dimensional Experimental Design and Kernel Bandits
Romain Camilleri
Julian Katz-Samuels
Kevin Jamieson
77
57
0
12 May 2021
On proportional volume sampling for experimental design in general
  spaces
On proportional volume sampling for experimental design in general spaces
Arnaud Poinas
Rémi Bardenet
46
5
0
09 Nov 2020
A Local Search Framework for Experimental Design
A Local Search Framework for Experimental Design
L. Lau
Hong Zhou
37
8
0
29 Oct 2020
Linear Bandits with Limited Adaptivity and Learning Distributional
  Optimal Design
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design
Yufei Ruan
Jiaqi Yang
Yuanshuo Zhou
OffRL
168
52
0
04 Jul 2020
Determinantal Point Processes in Randomized Numerical Linear Algebra
Determinantal Point Processes in Randomized Numerical Linear Algebra
Michal Derezinski
Michael W. Mahoney
78
81
0
07 May 2020
Maximizing Determinants under Matroid Constraints
Maximizing Determinants under Matroid Constraints
V. Madan
Aleksandar Nikolov
Mohit Singh
U. Tantipongpipat
57
8
0
16 Apr 2020
Best Principal Submatrix Selection for the Maximum Entropy Sampling
  Problem: Scalable Algorithms and Performance Guarantees
Best Principal Submatrix Selection for the Maximum Entropy Sampling Problem: Scalable Algorithms and Performance Guarantees
Yongchun Li
Weijun Xie
15
16
0
23 Jan 2020
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
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
105
25
0
04 Feb 2019
Iterative Projection and Matching: Finding Structure-preserving
  Representatives and Its Application to Computer Vision
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision
M. Joneidi
Alireza Zaeemzadeh
Nazanin Rahnavard
M. Shah
76
14
0
29 Nov 2018
Correcting the bias in least squares regression with volume-rescaled
  sampling
Correcting the bias in least squares regression with volume-rescaled sampling
Michal Derezinski
Manfred K. Warmuth
Daniel J. Hsu
51
15
0
04 Oct 2018
Composable Core-sets for Determinant Maximization Problems via Spectral
  Spanners
Composable Core-sets for Determinant Maximization Problems via Spectral Spanners
Piotr Indyk
S. Mahabadi
S. Gharan
A. Rezaei
60
20
0
31 Jul 2018
Leveraged volume sampling for linear regression
Leveraged volume sampling for linear regression
Michal Derezinski
Manfred K. Warmuth
Daniel J. Hsu
80
58
0
19 Feb 2018
Near-Optimal Discrete Optimization for Experimental Design: A Regret
  Minimization Approach
Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach
Zeyuan Allen-Zhu
Yuanzhi Li
Aarti Singh
Yining Wang
84
59
0
14 Nov 2017
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