Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Provably End-to-end Label-Noise Learning without Anchor Points   In label-noise learning, the transition matrix plays a key role in building
statistically consistent classifiers. Existing consistent estimators for the
transition matrix have been developed by exploiting anchor points. However, the
anchor-point assumption is not always satisfied in real scenarios. In this
paper, we propose an end-to-end framework for solving label-noise learning
without anchor points, in which we simultaneously optimize two objectives: the
cross entropy loss between the noisy label and the predicted probability by the
neural network, and the volume of the simplex formed by the columns of the
transition matrix. Our proposed framework can identify the transition matrix if
the clean class-posterior probabilities are sufficiently scattered. This is by
far the mildest assumption under which the transition matrix is provably
identifiable and the learned classifier is statistically consistent.
Experimental results on benchmark datasets demonstrate the effectiveness and
robustness of the proposed method.

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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
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The categories of this paper are: stat
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: astro-ph
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The categories of this paper are: physics
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an