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We tackle sequential learning under label noise in applications where a human supervisor can be queried to relabel suspicious examples.
Residuals and influence in regression
R Dennis Cook and Sanford Weisberg · 1982
Earlier work this paper cites.
Learning from noisy examples
Dana Angluin and Philip Laird · 1988
Earlier work this paper cites.
Using diaries in social research
Louise Corti · 1993
Earlier work this paper cites.
Exploiting generative models in discriminative classifiers
Tommi S Jaakkola, David Haussler, et al · 1999
Earlier work this paper cites.
The psychology of survey response
Roger Tourangeau, Lance J Rips, and Kenneth Rasinski · 2000
Earlier work this paper cites.
Theory of point estimation
Erich L Lehmann and George Casella · 2006
Earlier work this paper cites.
Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
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Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
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Luigi Malago, Nicolo Cesa-Bianchi, and J Renders · 2014
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Learning from crowdsourced labeled data: a survey
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Earlier work this paper cites.
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Earlier work this paper cites.
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Learning in the Wild with Incremental Skeptical Gaussian Processes
Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, and Andrea Passerini · 2020
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Influence functions in deep learning are fragile
Samyadeep Basu, Philip Pope, and Soheil Feizi · 2020
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Limitations of the empirical fisher approximation for natural gradient descent
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