43,185 research outputs found
The Power of Localization for Efficiently Learning Linear Separators with Noise
We introduce a new approach for designing computationally efficient learning
algorithms that are tolerant to noise, and demonstrate its effectiveness by
designing algorithms with improved noise tolerance guarantees for learning
linear separators.
We consider both the malicious noise model and the adversarial label noise
model. For malicious noise, where the adversary can corrupt both the label and
the features, we provide a polynomial-time algorithm for learning linear
separators in under isotropic log-concave distributions that can
tolerate a nearly information-theoretically optimal noise rate of . For the adversarial label noise model, where the
distribution over the feature vectors is unchanged, and the overall probability
of a noisy label is constrained to be at most , we also give a
polynomial-time algorithm for learning linear separators in under
isotropic log-concave distributions that can handle a noise rate of .
We show that, in the active learning model, our algorithms achieve a label
complexity whose dependence on the error parameter is
polylogarithmic. This provides the first polynomial-time active learning
algorithm for learning linear separators in the presence of malicious noise or
adversarial label noise.Comment: Contains improved label complexity analysis communicated to us by
Steve Hannek
Efficient Learning of Linear Separators under Bounded Noise
We study the learnability of linear separators in in the presence of
bounded (a.k.a Massart) noise. This is a realistic generalization of the random
classification noise model, where the adversary can flip each example with
probability . We provide the first polynomial time algorithm
that can learn linear separators to arbitrarily small excess error in this
noise model under the uniform distribution over the unit ball in , for
some constant value of . While widely studied in the statistical learning
theory community in the context of getting faster convergence rates,
computationally efficient algorithms in this model had remained elusive. Our
work provides the first evidence that one can indeed design algorithms
achieving arbitrarily small excess error in polynomial time under this
realistic noise model and thus opens up a new and exciting line of research.
We additionally provide lower bounds showing that popular algorithms such as
hinge loss minimization and averaging cannot lead to arbitrarily small excess
error under Massart noise, even under the uniform distribution. Our work
instead, makes use of a margin based technique developed in the context of
active learning. As a result, our algorithm is also an active learning
algorithm with label complexity that is only a logarithmic the desired excess
error
The nature of the animacy organization in human ventral temporal cortex
The principles underlying the animacy organization of the ventral temporal
cortex (VTC) remain hotly debated, with recent evidence pointing to an animacy
continuum rather than a dichotomy. What drives this continuum? According to the
visual categorization hypothesis, the continuum reflects the degree to which
animals contain animal-diagnostic features. By contrast, the agency hypothesis
posits that the continuum reflects the degree to which animals are perceived as
(social) agents. Here, we tested both hypotheses with a stimulus set in which
visual categorizability and agency were dissociated based on representations in
convolutional neural networks and behavioral experiments. Using fMRI, we found
that visual categorizability and agency explained independent components of the
animacy continuum in VTC. Modeled together, they fully explained the animacy
continuum. Finally, clusters explained by visual categorizability were
localized posterior to clusters explained by agency. These results show that
multiple organizing principles, including agency, underlie the animacy
continuum in VTC.Comment: 16 pages, 5 figures, code+data at -
https://doi.org/10.17605/OSF.IO/VXWG9 Update - added supplementary results
and edited abstrac
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