Learning with an unreliable teacher

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning kernel logistic regression in the presence of class label noise (2014, background): “Part of the reason for this may be that uniform/symmetric label noise is relatively harmless [21,22,12,27].”
Data Programming: Creating Large Training Sets, Quickly (2016, background): “The general case of learning with noisy labels is treated both in classical [18] and more recent contexts [21].”
Supervised learning from multiple experts: whom to trust when everyone lies a bit (2009, background): “There is also some theoretical work (see (Lugosi, 1992) and reference therein) dealing with multiple experts.”
Label-Noise Robust Logistic Regression and Its Applications (2012, background): “Since it has been shown theoretically that symmetric label noise is relatively harmless, for example see [18], here only asymmetric label noise of various levels was artificially injected for the purpose of systematic testing.”
Learning From Crowds (2010, background): “There is also some theoretical work (see Lugosi, 1992 and Dekel and Shamir, 2009a) dealing with multiple experts.”
Get another label? improving data quality and data mining using multiple, noisy labelers (2008, background): “Prior research has addressed important problems necessary for a full labeling solution that uses multiple noisy labelers, such as estimating the quality of labelers [6, 26, 28], and learning with uncertain labels [13, 24, 25].”