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Robust loss minimization is an important strategy for handling robust learning issue on noisy labels.
Learning to control fast-weight memories: An alternative to dynamic recurrent networks
Jürgen Schmidhuber · 1992
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Convexity, classification, and risk bounds
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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On the design of loss functions for classification: theory, robustness to outliers, and savageboost
Hamed Masnadi-Shirazi and Nuno Vasconcelos · 2009
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On the efficient minimization of classification calibrated surrogates
Richard Nock and Frank Nielsen · 2009
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Self-paced learning for latent variable models
M Pawan Kumar, Benjamin Packer, and Daphne Koller · 2010
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Support vector machines with the ramp loss and the hard margin loss
J Paul Brooks · 2011
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Robust statistics
Peter J Huber · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Noise tolerance under risk minimization
Naresh Manwani and PS Sastry · 2013
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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Multi-task bayesian optimization
Kevin Swersky, Jasper Snoek, and Ryan P Adams · 2013
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Learning to predict from crowdsourced data
Wei Bi, Liwei Wang, James T Kwok, and Zhuowen Tu · 2014
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Easy samples first: Self-paced reranking for zero-example multimedia search
Lu Jiang, Deyu Meng, Teruko Mitamura, and Alexander G Hauptmann · 2014
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Robust classification under sample selection bias
Anqi Liu and Brian Ziebart · 2014
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Making risk minimization tolerant to label noise
Aritra Ghosh, Naresh Manwani, and PS Sastry · 2015
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 2015
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Training deep neural networks on noisy labels with bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2015
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Training convolutional networks with noisy labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus · 2015
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Learning with symmetric label noise: The importance of being unhinged
Brendan Van Rooyen, Aditya Menon, and Robert C Williamson · 2015
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Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
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Self-paced learning for matrix factorization
Qian Zhao, Deyu Meng, Lu Jiang, Qi Xie, Zongben Xu, and Alexander G Hauptmann · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Attend in groups: a weakly-supervised deep learning framework for learning from web data
Bohan Zhuang, Lingqiao Liu, Yao Li, Chunhua Shen, and Ian Reid · 2017
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Bilevel programming for hyperparameter optimization and meta-learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil · 2018
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Decomposition-based evolutionary multiobjective optimization to self-paced learning
Maoguo Gong, Hao Li, Deyu Meng, Qiguang Miao, and Jia Liu · 2018
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Using trusted data to train deep networks on labels corrupted by severe noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel · 2018
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Learning to detect concepts from webly-labeled video data
Junwei Liang, Lu Jiang, Deyu Meng, and Alexander G Hauptmann · 2016
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Hyperparameter optimization with approximate gradient
Fabian Pedregosa · 2016
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Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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Forward and reverse gradient-based hyperparameter optimization
Luca Franceschi, Michele Donini, Paolo Frasconi, and Massimiliano Pontil · 2017
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
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Cleannet: Transfer learning for scalable image classifier training with label noise
Kuang-Huei Lee, Xiaodong He, Lei Zhang, and Linjun Yang · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Small sample learning in big data era
Jun Shu, Zongben Xu, and Deyu Meng · 2018
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Joint optimization framework for learning with noisy labels
Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa · 2018
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Learning to teach with dynamic loss functions
Lijun Wu, Fei Tian, Yingce Xia, Yang Fan, Tao Qin, Lai Jian-Huang, and Tie-Yan Liu · 2018
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Generalized cross entropy loss for training deep neural networks with noisy labels
Zhilu Zhang and Mert Sabuncu · 2018
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Robust bi-tempered logistic loss based on bregman divergences
Ehsan Amid, Manfred K Warmuth, Rohan Anil, and Tomer Koren · 2019
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Josif Grabocka, Randolf Scholz, and Lars Schmidt-Thieme · 2019
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Addressing the loss-metric mismatch with adaptive loss alignment
Chen Huang, Shuangfei Zhai, Walter Talbott, Miguel Bautista Martin, Shih-Yu Sun, Carlos Guestrin, and Josh Susskind · 2019
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Curriculum loss: Robust learning and generalization against label corruption
Yueming Lyu and Ivor W Tsang · 2019
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Meta-weight-net: Learning an explicit mapping for sample weighting
Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng · 2019
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Symmetric cross entropy for robust learning with noisy labels
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey · 2019
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Autoloss: Learning discrete schedules for alternate optimization
Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, and Eric Xing · 2019
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L_dmi: An information-theoretic noise-robust loss function
Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang · 2019
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