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In this work, we initiate the study of one-round active learning, which aims to select a subset of unlabeled data points that achieve the highest model performance after being labeled with only the information from initially labeled data points.
Deep batch active learning by diverse, uncertain gradient lower bounds
Ash, J. T.; Zhang, C.; Krishnamurthy, A.; Langford, J.; and Agarwal, A. 2019a · 1906
Earlier work this paper cites.
Deep batch active learning by diverse, uncertain gradient lower bounds
Ash, J. T.; Zhang, C.; Krishnamurthy, A.; Langford, J.; and Agarwal, A. 2019b · 1906
Earlier work this paper cites.
Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Kirsch, A.; Van Amersfoort, J.; and Gal, Y. 2019 · 1906
Earlier work this paper cites.
Efficient task-specific data valuation for nearest neighbor algorithms
Jia, R.; Dao, D.; Wang, B.; Hubis, F. A.; Gurel, N. M.; Li, B.; Zhang, C.; Spanos, C. J.; and Song, D. 2019 · 1908
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. 2019 · 1912
Earlier work this paper cites.
Submodularity in data subset selection and active learning
Wei, K.; Iyer, R.; and Bilmes, J. 2015 · 1963
Earlier work this paper cites.
Accelerated greedy algorithms for maximizing submodular set functions
Minoux, M. 1978 · 1978
Earlier work this paper cites.
Query by committee
Seung, H. S.; Opper, M.; and Sompolinsky, H. 1992 · 1992
Earlier work this paper cites.
A sequential algorithm for training text classifiers
Lewis, D. D.; and Gale, W. A. 1994 · 1994
Earlier work this paper cites.
Selective sampling using the query by committee algorithm
Freund, Y.; Seung, H. S.; Shamir, E.; and Tishby, N. 1997 · 1997
Earlier work this paper cites.
Optical recognition of handwritten digits data set
Alpaydin, E.; and Kaynak, C. 1998 · 1998
Earlier work this paper cites.
A threshold of ln n for approximating set cover
Feige, U. 1998 · 1998
Earlier work this paper cites.
The MNIST database of handwritten digits
LeCun, Y. 1998 · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; and Haffner, P. 1998 · 1998
Earlier work this paper cites.
Query learning with large margin classifiers
Campbell, C.; Cristianini, N.; Smola, A.; et al. 2000 · 2000
Cited alongside, same era.
The kernel gibbs sampler
Graepel, T.; and Herbrich, R. 2000 · 2000
Cited alongside, same era.
Less is more: Active learning with support vector machines
Schohn, G.; and Cohn, D. 2000 · 2000
Cited alongside, same era.
Support vector machine active learning with applications to text classification
Tong, S.; and Koller, D. 2001 · 2001
Cited alongside, same era.
Query by committee, linear separation and random walks
Fine, S.; Gilad-Bachrach, R.; and Shamir, E. 2002 · 2002
Cited alongside, same era.
Language models are few-shot learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 2005
Submodular maximization with cardinality constraints
Buchbinder, N.; Feldman, M.; Naor, J.; and Schwartz, R. 2014 · 2014
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Fast multi-stage submodular maximization
Wei, K.; Iyer, R.; and Bilmes, J. 2014 · 2014
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Unsupervised submodular subset selection for speech data
Wei, K.; Liu, Y.; Kirchhoff, K.; and Bilmes, J. 2014 · 2014
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Summarization of multi-document topic hierarchies using submodular mixtures
Bairi, R.; Iyer, R.; Ramakrishnan, G.; and Bilmes, J. 2015 · 2015
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Lazier than lazy greedy
Mirzasoleiman, B.; Badanidiyuru, A.; Karbasi, A.; Vondrák, J.; and Krause, A. 2015 · 2015
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Optimal bounds on approximation of submodular and XOS functions by juntas
Feldman, V.; and Vondrák, J. 2016 · 2016
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Cited alongside, same era.
Batch mode active learning and its application to medical image classification
Hoi, S. C.; Jin, R.; Zhu, J.; and Lyu, M. R. 2006 · 2006
Cited alongside, same era.
Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
Cited alongside, same era.
Active learning literature survey
Settles, B. 2009 · 2009
Cited alongside, same era.
Learning submodular functions
Balcan, M.-F.; and Harvey, N. J. 2011 · 2011
Cited alongside, same era.
Scikit-learn: Machine learning in Python
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; et al. 2011 · 2011
Cited alongside, same era.
Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook
Pinto, N.; Stone, Z.; Zickler, T.; and Cox, D. 2011 · 2011
Cited alongside, same era.
Maximization of approximately submodular functions
Horel, T.; and Singer, Y. 2016 · 2016
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Active learning for convolutional neural networks: A core-set approach
Sener, O.; and Savarese, S. 2017 · 2017
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Zaheer, M.; Kottur, S.; Ravanbakhsh, S.; Poczos, B.; Salakhutdinov, R.; and Smola, A. 2017 · 2017
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Optimization for approximate submodularity
Hassidim, A.; and Singer, Y. 2018 · 2018
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Tight bounds on l1 approximation and learning of self-bounding functions
Feldman, V.; Kothari, P.; and Vondrák, J. 2020 · 2020
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Learnability of Learning Performance and Its Application to Data Valuation
Wang, T.; Yang, Y.; and Jia, R. 2021 · 2021
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Towards understanding end-to-end learning in the context of data: machine learning dancing over semirings & Codd’s table
Wu, W.; and Zhang, C. 2021 · 2021
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