74 research outputs found
Making Risk Minimization Tolerant to Label Noise
In many applications, the training data, from which one needs to learn a
classifier, is corrupted with label noise. Many standard algorithms such as SVM
perform poorly in presence of label noise. In this paper we investigate the
robustness of risk minimization to label noise. We prove a sufficient condition
on a loss function for the risk minimization under that loss to be tolerant to
uniform label noise. We show that the loss, sigmoid loss, ramp loss and
probit loss satisfy this condition though none of the standard convex loss
functions satisfy it. We also prove that, by choosing a sufficiently large
value of a parameter in the loss function, the sigmoid loss, ramp loss and
probit loss can be made tolerant to non-uniform label noise also if we can
assume the classes to be separable under noise-free data distribution. Through
extensive empirical studies, we show that risk minimization under the
loss, the sigmoid loss and the ramp loss has much better robustness to label
noise when compared to the SVM algorithm
A New PAC-Bayesian Perspective on Domain Adaptation
We study the issue of PAC-Bayesian domain adaptation: We want to learn, from
a source domain, a majority vote model dedicated to a target one. Our
theoretical contribution brings a new perspective by deriving an upper-bound on
the target risk where the distributions' divergence---expressed as a
ratio---controls the trade-off between a source error measure and the target
voters' disagreement. Our bound suggests that one has to focus on regions where
the source data is informative.From this result, we derive a PAC-Bayesian
generalization bound, and specialize it to linear classifiers. Then, we infer a
learning algorithmand perform experiments on real data.Comment: Published at ICML 201
PAC-Bayes and Domain Adaptation
We provide two main contributions in PAC-Bayesian theory for domain
adaptation where the objective is to learn, from a source distribution, a
well-performing majority vote on a different, but related, target distribution.
Firstly, we propose an improvement of the previous approach we proposed in
Germain et al. (2013), which relies on a novel distribution pseudodistance
based on a disagreement averaging, allowing us to derive a new tighter domain
adaptation bound for the target risk. While this bound stands in the spirit of
common domain adaptation works, we derive a second bound (introduced in Germain
et al., 2016) that brings a new perspective on domain adaptation by deriving an
upper bound on the target risk where the distributions' divergence-expressed as
a ratio-controls the trade-off between a source error measure and the target
voters' disagreement. We discuss and compare both results, from which we obtain
PAC-Bayesian generalization bounds. Furthermore, from the PAC-Bayesian
specialization to linear classifiers, we infer two learning algorithms, and we
evaluate them on real data.Comment: Neurocomputing, Elsevier, 2019. arXiv admin note: substantial text
overlap with arXiv:1503.0694
PAC-Bayesian Theory Meets Bayesian Inference
We exhibit a strong link between frequentist PAC-Bayesian risk bounds and the
Bayesian marginal likelihood. That is, for the negative log-likelihood loss
function, we show that the minimization of PAC-Bayesian generalization risk
bounds maximizes the Bayesian marginal likelihood. This provides an alternative
explanation to the Bayesian Occam's razor criteria, under the assumption that
the data is generated by an i.i.d distribution. Moreover, as the negative
log-likelihood is an unbounded loss function, we motivate and propose a
PAC-Bayesian theorem tailored for the sub-gamma loss family, and we show that
our approach is sound on classical Bayesian linear regression tasks.Comment: Published at NIPS 2015
(http://papers.nips.cc/paper/6569-pac-bayesian-theory-meets-bayesian-inference
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