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Found 6 papers, 1 papers with code
Learning from Binary Labels with Instance-Dependent Corruption
Suppose we have a sample of instances paired with binary labels corrupted by arbitrary instance- and label-dependent noise.
An Average Classification Algorithm
When working with a high or infinite dimensional kernel, it is imperative for speed of evaluation and storage issues that as few training samples as possible are used in the kernel expansion.
Learning with Symmetric Label Noise: The Importance of Being Unhinged
However, Long and Servedio [2010] proved that under symmetric label noise (SLN), minimisation of any convex potential over a linear function class can result in classification performance equivalent to random guessing.
A Theory of Feature Learning
Feature Learning aims to extract relevant information contained in data sets in an automated fashion.
Learning in the Presence of Corruption
In this paper we develop a general framework for tackling such problems as well as introducing upper and lower bounds on the risk for learning in the presence of corruption.
Le Cam meets LeCun: Deficiency and Generic Feature Learning
"Deep Learning" methods attempt to learn generic features in an unsupervised fashion from a large unlabelled data set.
