
Learning with Bounded Instance and Labeldependent Label Noise
Instance and labeldependent label noise (ILN) is widely existed in rea...
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Twostage Training for Learning from Label Proportions
Learning from label proportions (LLP) aims at learning an instancelevel...
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Probabilistic Detection and Estimation of Conic Sections from Noisy Data
Inferring unknown conic sections on the basis of noisy data is a challen...
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Learning about individuals from group statistics
We propose a new problem formulation which is similar to, but more infor...
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Latent Distribution Assumption for Unbiased and Consistent Consensus Modelling
We study the problem of aggregation noisy labels. Usually, it is solved ...
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Cautious Deep Learning
Most classifiers operate by selecting the maximum of an estimate of the ...
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Learning from Label Proportions by Optimizing Cluster Model Selection
In a supervised learning scenario, we learn a mapping from input to outp...
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Learning from Noisy Label Distributions
In this paper, we consider a novel machine learning problem, that is, learning a classifier from noisy label distributions. In this problem, each instance with a feature vector belongs to at least one group. Then, instead of the true label of each instance, we observe the label distribution of the instances associated with a group, where the label distribution is distorted by an unknown noise. Our goals are to (1) estimate the true label of each instance, and (2) learn a classifier that predicts the true label of a new instance. We propose a probabilistic model that considers true label distributions of groups and parameters that represent the noise as hidden variables. The model can be learned based on a variational Bayesian method. In numerical experiments, we show that the proposed model outperforms existing methods in terms of the estimation of the true labels of instances.
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