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Active learning theories and methods have been extensively studied in classical statistical learning settings.
A new measure of rank correlation
Maurice G Kendall · 1938
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Improving generalization with active learning
David Cohn, Les Atlas, and Richard Ladner · 1994
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Toward optimal active learning through sampling estimation of error reduction
Nicholas Roy and Andrew McCallum · 2001
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
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Integrating structured biological data by kernel maximum mean discrepancy
Karsten M Borgwardt, Arthur Gretton, Malte J Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, and Alex J Smola · 2006
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Margin-based active learning for structured output spaces
Dan Roth and Kevin Small · 2006
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Margin based active learning
Maria-Florina Balcan, Andrei Z. Broder, and Tong Zhang · 2007
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Multiple-instance active learning
Burr Settles, Mark Craven, and Soumya Ray · 2007
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Hierarchical sampling for active learning
Sanjoy Dasgupta and Daniel Hsu · 2008
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Active learning literature survey
Burr Settles · 2009
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Agnostic active learning
Maria-Florina Balcan, Alina Beygelzimer, and John Langford · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Reading digits in natural images with unsupervised feature learning, 2011
Yuval Netzer, Tao Wang, Adam Coates, Ro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Chapter 12: Significance and measures of association
R Botsch · 2011
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Uncertainty-based active learning with instability estimation for text classification
Jingbo Zhu and Matthew Ma · 2012
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Active and passive learning of linear separators under log-concave distributions
Maria-Florina Balcan and Philip M. Long · 2013
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Theory of disagreement-based active learning
Steve Hanneke et al · 2014
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A new active labeling method for deep learning
Dan Wang and Yi Shang · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Querying discriminative and representative samples for batch mode active learning
Zheng Wang and Jieping Ye · 2015
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Exploring representativeness and informativeness for active learning
Bo Du, Zengmao Wang, Lefei Zhang, Liangpei Zhang, Wei Liu, Jialie Shen, and Dacheng Tao · 2015
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Active learning by learning
Wei-Ning Hsu and Hsuan-Tien Lin · 2015
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Train faster, generalize better: Stability of stochastic gradient descent
Moritz Hardt, Ben Recht, and Yoram Singer · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Active learning for speech recognition: the power of gradients
Jiaji Huang, Rewon Child, Vinay Rao, Hairong Liu, Sanjeev Satheesh, and Adam Coates · 2016
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Active learning using uncertainty information
Yazhou Yang and Marco Loog · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Algorithmic stability and hypothesis complexity
Tongliang Liu, Gábor Lugosi, Gergely Neu, and Dacheng Tao · 2017
A bayesian perspective on training speed and model selection
Clare Lyle, Lisa Schut, Robin Ru, Yarin Gal, and Mark van der Wilk · 2020
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Revisiting the train loss: an efficient performance estimator for neural architecture search
Binxin Ru, Clare Lyle, Lisa Schut, Mark van der Wilk, and Yarin Gal · 2020
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Gradients as features for deep representation learning
Fangzhou Mu, Yingyu Liang, and Yin Li · 2020
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The recurrent neural tangent kernel
Sina Alemohammad, Zichao Wang, Randall Balestriero, and Richard Baraniuk · 2020
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On the neural tangent kernel of deep networks with orthogonal initialization
Wei Huang, Weitao Du, and Richard Yi Da Xu · 2020
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Cited alongside, same era.
Neural tangent kernel: convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler · 2018
Cited alongside, same era.
Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, and Silvio Savarese · 2018
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Foundations of machine learning
Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Active learning in the overparameterized and interpolating regime
Mina Karzand and Robert D. Nowak · 2019
Cited alongside, same era.
BackPACK: Packing more into backprop
Felix Dangel, Frederik Kunstner, and Philipp Hennig · 2020
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Finite versus infinite neural networks: an empirical study
Jaehoon Lee, Samuel Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, and Jascha Sohl-Dickstein · 2020
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Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the neural tangent kernel
Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M Roy, and Surya Ganguli · 2020
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Towards nngp-guided neural architecture search
Daniel S Park, Jaehoon Lee, Daiyi Peng, Yuan Cao, and Jascha Sohl-Dickstein · 2020
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Optimization of graph neural networks: Implicit acceleration by skip connections and more depth
Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, and Kenji Kawaguchi · 2021
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Influence selection for active learning
Zhuoming Liu, Hao Ding, Huaping Zhong, Weijia Li, Jifeng Dai, and Conghui He · 2021
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Egl++: Extending expected gradient length to active learning for human pose estimation
Megh Shukla · 2021
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Efficient statistical tests: A neural tangent kernel approach
Sheng Jia, Ehsan Nezhadarya, Yuhuai Wu, and Jimmy Ba · 2021
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Towards deepening graph neural networks: A gntk-based optimization perspective
Wei Huang, Yayong Li, Weitao Du, Richard Yi Da Xu, Jie Yin, Ling Chen, and Miao Zhang · 2021
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Neural active learning with performance guarantees
Pranjal Awasthi, Christoph Dann, Claudio Gentile, Ayush Sekhari, and Zhilei Wang · 2021
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Enabling fast differentially private sgd via just-in-time compilation and vectorization
Pranav Subramani, Nicholas Vadivelu, and Gautam Kamath · 2021
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Scaling neural tangent kernels via sketching and random features
Amir Zandieh, Insu Han, Haim Avron, Neta Shoham, Chaewon Kim, and Jinwoo Shin · 2021
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Neural architecture search on imagenet in four gpu hours: A theoretically inspired perspective
Wuyang Chen, Xinyu Gong, and Zhangyang Wang · 2021
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When vision transformers outperform resnets without pre-training or strong data augmentations
Xiangning Chen, Cho-Jui Hsieh, and Boqing Gong · 2021
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A linearized framework and a new benchmark for model selection for fine-tuning
Aditya Deshpande, Alessandro Achille, Avinash Ravichandran, Hao Li, Luca Zancato, Charless Fowlkes, Rahul Bhotika, Stefano Soatto, and Pietro Perona · 2021
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Demystify optimization and generalization of over-parameterized pac-bayesian learning
Wei Huang, Chunrui Liu, Yilan Chen, Tianyu Liu, and Richard Yi Da Xu · 2022
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Improved algorithms for neural active learning
Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, and Jingrui He · 2022
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Fishr: Invariant gradient variances for out-of-distribution generalization
Alexandre Rame, Corentin Dancette, and Matthieu Cord · 2022
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