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Leveraged volume sampling for linear regression
v1v2v3 (latest)

Leveraged volume sampling for linear regression

19 February 2018
Michal Derezinski
Manfred K. Warmuth
Daniel J. Hsu
ArXiv (abs)PDFHTML

Papers citing "Leveraged volume sampling for linear regression"

30 / 30 papers shown
Title
Robust Offline Active Learning on Graphs
Robust Offline Active Learning on Graphs
Yuanchen Wu
Yubai Yuan
OffRL
48
0
0
15 Aug 2024
Recent and Upcoming Developments in Randomized Numerical Linear Algebra
  for Machine Learning
Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning
Michał Dereziński
Michael W. Mahoney
81
11
0
17 Jun 2024
Agnostic Active Learning of Single Index Models with Linear Sample
  Complexity
Agnostic Active Learning of Single Index Models with Linear Sample Complexity
Aarshvi Gajjar
Wai Ming Tai
Xingyu Xu
Chinmay Hegde
Yi Li
Chris Musco
84
9
0
15 May 2024
A unified framework for learning with nonlinear model classes from
  arbitrary linear samples
A unified framework for learning with nonlinear model classes from arbitrary linear samples
Ben Adcock
Juan M. Cardenas
N. Dexter
75
3
0
25 Nov 2023
Improved Active Learning via Dependent Leverage Score Sampling
Improved Active Learning via Dependent Leverage Score Sampling
Atsushi Shimizu
Xiaoou Cheng
Chris Musco
Jonathan Weare
FedML
59
6
0
08 Oct 2023
CS4ML: A general framework for active learning with arbitrary data based
  on Christoffel functions
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions
Ben Adcock
Juan M. Cardenas
N. Dexter
70
6
0
01 Jun 2023
Improved Financial Forecasting via Quantum Machine Learning
Improved Financial Forecasting via Quantum Machine Learning
Sohum Thakkar
Skander Kazdaghli
Natansh Mathur
Iordanis Kerenidis
A. J. Ferreira-Martins
Samurai Brito QC Ware Corp
AIFin
55
26
0
31 May 2023
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
Semi-supervised Active Regression
Semi-supervised Active Regression
Fnu Devvrit
Nived Rajaraman
Pranjal Awasthi
102
0
0
12 Jun 2021
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
Sparse sketches with small inversion bias
Sparse sketches with small inversion bias
Michal Derezinski
Zhenyu Liao
Yan Sun
Michael W. Mahoney
107
22
0
21 Nov 2020
On proportional volume sampling for experimental design in general
  spaces
On proportional volume sampling for experimental design in general spaces
Arnaud Poinas
Rémi Bardenet
49
5
0
09 Nov 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
Active Online Learning with Hidden Shifting Domains
Active Online Learning with Hidden Shifting Domains
Yining Chen
Haipeng Luo
Tengyu Ma
Chicheng Zhang
48
5
0
25 Jun 2020
Fourier Sparse Leverage Scores and Approximate Kernel Learning
Fourier Sparse Leverage Scores and Approximate Kernel Learning
T. Erdélyi
Cameron Musco
Christopher Musco
73
22
0
12 Jun 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
Kernel interpolation with continuous volume sampling
Kernel interpolation with continuous volume sampling
Ayoub Belhadji
Rémi Bardenet
P. Chainais
54
23
0
22 Feb 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
Random Quadratic Forms with Dependence: Applications to Restricted
  Isometry and Beyond
Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond
A. Banerjee
Qilong Gu
V. Sivakumar
Zhiwei Steven Wu
47
4
0
11 Oct 2019
Bayesian Batch Active Learning as Sparse Subset Approximation
Bayesian Batch Active Learning as Sparse Subset Approximation
Robert Pinsler
Jonathan Gordon
Eric T. Nalisnick
José Miguel Hernández-Lobato
UQCV
69
132
0
06 Aug 2019
Unbiased estimators for random design regression
Unbiased estimators for random design regression
Michal Derezinski
Manfred K. Warmuth
Daniel J. Hsu
73
17
0
08 Jul 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
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
A determinantal point process for column subset selection
A determinantal point process for column subset selection
Ayoub Belhadji
Rémi Bardenet
P. Chainais
43
28
0
23 Dec 2018
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
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
54
15
0
04 Oct 2018
Determinantal Point Processes for Coresets
Determinantal Point Processes for Coresets
Nicolas M Tremblay
Simon Barthelmé
P. Amblard
76
32
0
23 Mar 2018
Unbiased estimates for linear regression via volume sampling
Unbiased estimates for linear regression via volume sampling
Michal Derezinski
Manfred K. Warmuth
120
51
0
19 May 2017
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