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It is widely believed that given the same labeling budget, active learning (AL) algorithms like margin-based active learning achieve better predictive performance than passive learning (PL), albeit at a higher computational cost.
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D. C. Liu and J. Nocedal · 1989
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Improving generalization with active learning
D. Cohn, R. Ladner, and A. Waibel · 1994
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A sequential algorithm for training text classifiers
D. D. Lewis and W. A. Gale · 1994
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
J. C. Platt · 1999
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Less is more: Active learning with support vector machines
G. Schohn and D. Cohn · 2000
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Active learning of partially Hidden Markov Models
T. Scheffer and S. Wrobel · 2001
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Support vector machine active learning with applications to text classification
S. Tong and D. Koller · 2001
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Incorporating diversity in active learning with support vector machines
K. Brinker · 2003
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Margin based active learning
M.-F. Balcan, A. Broder, and T. Zhang · 2007
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Learning on the border: Active learning in imbalanced data classification
S. Ertekin, J. Huang, L. Bottou, and L. Giles · 2007
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Active learning for logistic regression: An evaluation
A. I. Schein and L. H. Ungar · 2007
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Active learning literature survey
B. Settles · 2009
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Agnostic active learning without constraints
A. Beygelzimer, D. J. Hsu, J. Langford, and T. Zhang · 2010
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Introduction to the non-asymptotic analysis of random matrices
R. Vershynin · 2010
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Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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On causal and anticausal learning
B. Scholkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, and J. Mooij · 2012
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A Statistical Theory of Active Learning
S. Hanneke · 2013
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OpenML: networked science in machine learning
J. Vanschoren, J. N. van Rijn, B. Bischl, and L. Torgo · 2013
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Active learning by querying informative and representative examples
S.-J. Huang, R. Jin, and Z.-H. Zhou · 2014
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Convergence rates of active learning for maximum likelihood estimation
K. Chaudhuri, S. M. Kakade, P. Netrapalli, and S. Sanghavi · 2015
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Multi-class active learning by uncertainty sampling with diversity maximization
Y. Yang, Z. Ma, F. Nie, X. Chang, and A. G. Hauptmann · 2015
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Efficient active learning of sparse halfspaces
C. Zhang · 2018
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Mixture Models and Applications
N. Bouguila and W. Fan · 2019
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Discriminative active learning
D. Gissin and S. Shalev-Shwartz · 2019
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The implicit bias of gradient descent on nonseparable data
Z. Ji and M. Telgarsky · 2019
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Robustness may be at odds with accuracy
D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry · 2019
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Benign overfitting in linear regression
P. L. Bartlett, P. M. Long, G. Lugosi, and A. Tsigler · 2020
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
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Deep Bayesian active learning with image data
Y. Gal, R. Islam, and Z. Ghahramani · 2017
Cited alongside, same era.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
P. Helber, B. Bischke, A. Dengel, and D. Borth · 2017
Cited alongside, same era.
Adversarial active learning for deep networks: a margin based approach
M. Ducoffe and F. Precioso · 2018
Cited alongside, same era.
On the relationship between data efficiency and error for uncertainty sampling
S. Mussmann and P. Liang · 2018
Cited alongside, same era.
A. Javanmard and M. Soltanolkotabi · 2020
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Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks
M. Li, M. Soltanolkotabi, and S. Oymak · 2020
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Adversarial sampling for active learning
C. Mayer and R. Timofte · 2020
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Deep active learning: Unified and principled method for query and training
C. Shui, F. Zhou, C. Gagné, and B. Wang · 2020
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Interpolation can hurt robust generalization even when there is no noise
K. Donhauser, A. Tifrea, M. Aerni, R. Heckel, and F. Yang · 2021
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On statistical bias in active learning: How and when to fix it
S. Farquhar, Y. Gal, and T. Rainforth · 2021
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Classification vs regression in overparameterized regimes: Does the loss function matter?
V. Muthukumar, A. Narang, V. Subramanian, M. Belkin, D. Hsu, and A. Sahai · 2021
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Self-training converts weak learners to strong learners in mixture models
S. Frei, D. Zou, Z. Chen, and Q. Gu · 2022
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Active learning on a budget: Opposite strategies suit high and low budgets
G. Hacohen, A. Dekel, and D. Weinshall · 2022
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Beyond neural scaling laws: beating power law scaling via data pruning
B. Sorscher, R. Geirhos, S. Shekhar, S. Ganguli, and A. S. Morcos · 2022
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