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MaxiMin Active Learning in Overparameterized Model Classes}
v1v2 (latest)

MaxiMin Active Learning in Overparameterized Model Classes}

IEEE Journal on Selected Areas in Information Theory (JSAIT), 2019
29 May 2019
Mina Karzand
Robert D. Nowak
ArXiv (abs)PDFHTML

Papers citing "MaxiMin Active Learning in Overparameterized Model Classes}"

9 / 9 papers shown
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active
  Learning
Poisson Reweighted Laplacian Uncertainty Sampling for Graph-based Active LearningSIAM Journal on Mathematics of Data Science (SIMODS), 2022
Kevin Miller
Jeff Calder
233
6
0
27 Oct 2022
Graph-based Active Learning for Semi-supervised Classification of SAR
  Data
Graph-based Active Learning for Semi-supervised Classification of SAR Data
Kevin Miller
John Mauro
Jason Setiadi
Xoaquin Baca
Zhan Shi
Jeff Calder
Andrea L. Bertozzi
217
23
0
31 Mar 2022
Model-Change Active Learning in Graph-Based Semi-Supervised Learning
Model-Change Active Learning in Graph-Based Semi-Supervised Learning
Kevin Miller
Andrea L. Bertozzi
SSL
131
17
0
14 Oct 2021
Neural Active Learning with Performance Guarantees
Neural Active Learning with Performance GuaranteesNeural Information Processing Systems (NeurIPS), 2021
Pranjal Awasthi
Christoph Dann
Claudio Gentile
Ayush Sekhari
Zhilei Wang
165
24
0
06 Jun 2021
Stopping Criterion for Active Learning Based on Error Stability
Stopping Criterion for Active Learning Based on Error Stability
Hideaki Ishibashi
H. Hino
137
13
0
05 Apr 2021
Active Learning: Problem Settings and Recent Developments
Active Learning: Problem Settings and Recent Developments
H. Hino
231
46
0
08 Dec 2020
Experimental Design for Overparameterized Learning with Application to
  Single Shot Deep Active Learning
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active LearningIEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020
N. Shoham
H. Avron
BDL
212
13
0
27 Sep 2020
Overcoming the curse of dimensionality with Laplacian regularization in
  semi-supervised learning
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learningNeural Information Processing Systems (NeurIPS), 2020
Vivien A. Cabannes
Loucas Pillaud-Vivien
Francis R. Bach
Alessandro Rudi
270
20
0
09 Sep 2020
Efficient Graph-Based Active Learning with Probit Likelihood via
  Gaussian Approximations
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations
Kevin Miller
Hao Li
Andrea L. Bertozzi
96
9
0
21 Jul 2020
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