TY  - GEN
A1  - Blanchard, Gilles
A1  - Scott, Clayton
T1  - Corrigendum to: Classification with asymmetric label noise
BT  - Consistency and maximal denoising
T2  - Electronic journal of statistics
N2  - We point out a flaw in Lemma 15 of [1]. We also indicate how the main results of that section are still valid using a modified argument.
Y1  - 2018
U6  - https://doi.org/10.1214/18-EJS1422
SN  - 1935-7524
VL  - 12
IS  - 1
SP  - 1779
EP  - 1781
PB  - Institute of Mathematical Statistics
CY  - Cleveland
ER  - 
TY  - JOUR
A1  - Katz-Samuels, Julian
A1  - Blanchard, Gilles
A1  - Scott, Clayton
T1  - Decontamination of Mutual Contamination Models
JF  - Journal of machine learning research
N2  - Many machine learning problems can be characterized by mutual contamination models. In these problems, one observes several random samples from different convex combinations of a set of unknown base distributions and the goal is to infer these base distributions. This paper considers the general setting where the base distributions are defined on arbitrary probability spaces. We examine three popular machine learning problems that arise in this general setting: multiclass classification with label noise, demixing of mixed membership models, and classification with partial labels. In each case, we give sufficient conditions for identifiability and present algorithms for the infinite and finite sample settings, with associated performance guarantees.
KW  - multiclass classification with label noise
KW  - classification with partial labels
KW  - mixed membership models
KW  - topic modeling
KW  - mutual contamination models
Y1  - 2019
UR  - http://arxiv.org/abs/1710.01167
SN  - 1532-4435
VL  - 20
PB  - Microtome Publishing
CY  - Cambridge, Mass.
ER  - 
TY  - JOUR
A1  - Blanchard, Gilles
A1  - Flaska, Marek
A1  - Handy, Gregory
A1  - Pozzi, Sara
A1  - Scott, Clayton
T1  - Classification with asymmetric label noise: Consistency and maximal denoising
JF  - Electronic journal of statistics
N2  - In many real-world classification problems, the labels of training examples are randomly corrupted. Most previous theoretical work on classification with label noise assumes that the two classes are separable, that the label noise is independent of the true class label, or that the noise proportions for each class are known. In this work, we give conditions that are necessary and sufficient for the true class-conditional distributions to be identifiable. These conditions are weaker than those analyzed previously, and allow for the classes to be nonseparable and the noise levels to be asymmetric and unknown. The conditions essentially state that a majority of the observed labels are correct and that the true class-conditional distributions are "mutually irreducible," a concept we introduce that limits the similarity of the two distributions. For any label noise problem, there is a unique pair of true class-conditional distributions satisfying the proposed conditions, and we argue that this pair corresponds in a certain sense to maximal denoising of the observed distributions. Our results are facilitated by a connection to "mixture proportion estimation," which is the problem of estimating the maximal proportion of one distribution that is present in another. We establish a novel rate of convergence result for mixture proportion estimation, and apply this to obtain consistency of a discrimination rule based on surrogate loss minimization. Experimental results on benchmark data and a nuclear particle classification problem demonstrate the efficacy of our approach.
KW  - Classification
KW  - label noise
KW  - mixture proportion estimation
KW  - surrogate loss
KW  - consistency
Y1  - 2016
U6  - https://doi.org/10.1214/16-EJS1193
SN  - 1935-7524
VL  - 10
SP  - 2780
EP  - 2824
PB  - Institute of Mathematical Statistics
CY  - Cleveland
ER  -