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Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain.
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Yann LeCun · 1998
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Active learning literature survey
Burr Settles · 2009
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On the foundations of noise-free selective classification
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Active learning by querying informative and representative examples
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Two faces of active learning
Sanjoy Dasgupta · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Deep speech: Scaling up end-to-end speech recognition
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Domain-adversarial training of neural networks
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Boosted convolutional neural networks
Mohammad Moghimi, Serge J Belongie, Mohammad J Saberian, Jian Yang, Nuno Vasconcelos, and Li-Jia Li · 2016
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2017
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The power of ensembles for active learning in image classification
William H Beluch, Tim Genewein, Andreas Nürnberger, and Jan M Köhler · 2018
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Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
Estimating generalization under distribution shifts via domain-invariant representations
Ching-Yao Chuang, Antonio Torralba, and Stefanie Jegelka · 2020
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Selective question answering under domain shift
Amita Kamath, Robin Jia, and Percy Liang · 2020
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Consistent estimators for learning to defer to an expert
Hussein Mozannar and David Sontag · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li · 2020
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Active adversarial domain adaptation
Jong-Chyi Su, Yi-Hsuan Tsai, Kihyuk Sohn, Buyu Liu, Subhransu Maji, and Manmohan Chandraker · 2020
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Cinic-10 is not imagenet or cifar-10
Luke N Darlow, Elliot J Crowley, Antreas Antoniou, and Amos J Storkey · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Adversarial active learning for deep networks: a margin based approach
Melanie Ducoffe and Frederic Precioso · 2018
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Conditional adversarial domain adaptation
Mingsheng Long, Zhangjie Cao, Jianmin Wang, and Michael I Jordan · 2018
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Knowledge distillation by on-the-fly native ensemble
Xiatian Zhu, Shaogang Gong, et al · 2018
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Deep batch active learning by diverse, uncertain gradient lower bounds
Jordan T Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford, and Alekh Agarwal · 2019
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Theoretical analysis of self-training with deep networks on unlabeled data
Colin Wei, Kendrick Shen, Yining Chen, and Tengyu Ma · 2020
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Making Decisions Under High Stakes: Trustworthy and Expressive Bayesian Deep Learning
Wanqian Yang · 2020
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Cold-start active learning through self-supervised language modeling
Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber · 2020
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Detecting errors and estimating accuracy on unlabeled data with self-training ensembles
Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, and Somesh Jha · 2021
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Transferable query selection for active domain adaptation
Bo Fu, Zhangjie Cao, Jianmin Wang, and Mingsheng Long · 2021
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Doctor: A simple method for detecting misclassification errors
Federica Granese, Marco Romanelli, Daniele Gorla, Catuscia Palamidessi, and Pablo Piantanida · 2021
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Andreas Kirsch, Tom Rainforth, and Yarin Gal · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Active domain adaptation via clustering uncertainty-weighted embeddings
Viraj Prabhu, Arjun Chandrasekaran, Kate Saenko, and Judy Hoffman · 2021
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Extending the wilds benchmark for unsupervised adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, et al · 2021
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Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, and Mohammad Sabokrou · 2021
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Boost neural networks by checkpoints
Feng Wang, Guoyizhe Wei, Qiao Liu, Jinxiang Ou, Hairong Lv, et al · 2021
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Active learning under label shift
Eric Zhao, Anqi Liu, Animashree Anandkumar, and Yisong Yue · 2021
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Active learning on a budget: Opposite strategies suit high and low budgets
Guy Hacohen, Avihu Dekel, and Daphna Weinshall · 2022
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Diverse lottery tickets boost ensemble from a single pretrained model
Sosuke Kobayashi, Shun Kiyono, Jun Suzuki, and Kentaro Inui · 2022
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Deep unsupervised domain adaptation: A review of recent advances and perspectives
Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing, Hyejin Oh, Georges El Fakhri, Je-Won Kang, Jonghye Woo, et al · 2022
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Selective classification via neural network training dynamics
Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, and Nicolas Papernot · 2022
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Wild-time: A benchmark of in-the-wild distribution shift over time
Huaxiu Yao, Caroline Choi, Bochuan Cao, Yoonho Lee, Pang Wei Koh, and Chelsea Finn · 2022
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