Weakly supervised learning via statistical sufficiency
Date
2016
Authors
Patrini, Giorgio
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Abstract
The Thesis introduces a novel algorithmic framework for
weakly supervised learn- ing, namely, for any any problem in
between supervised and unsupervised learning, from the labels
standpoint. Weak supervision is the reality in many applications
of machine learning where training is performed with partially
missing, aggregated- level and/or noisy labels. The approach is
grounded on the concept of statistical suf- ficiency and its
transposition to loss functions. Our solution is problem-agnostic
yet constructive as it boils down to a simple two-steps
procedure. First, estimate a suffi- cient statistic for the
labels from weak supervision. Second, plug the estimate into a
(newly defined) linear-odd loss function and learn the model by
any gradient-based solver, with a simple adaptation. We apply the
same approach to several challeng- ing learning problems: (i)
learning from label proportions, (ii) learning with noisy labels
for both linear classifiers and deep neural networks, and (iii)
learning from feature-wise distributed datasets where the entity
matching function is unknown.
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Keywords
machine learning, weakly supervised learning, sufficient statistics, learning theory, noisy label, deep learning
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Type
Thesis (PhD)
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DOI
10.25911/5d723bc2607e3