260,642 research outputs found
A survey on domain adaptation theory: learning bounds and theoretical guarantees
All famous machine learning algorithms that comprise both supervised and
semi-supervised learning work well only under a common assumption: the training
and test data follow the same distribution. When the distribution changes, most
statistical models must be reconstructed from newly collected data, which for
some applications can be costly or impossible to obtain. Therefore, it has
become necessary to develop approaches that reduce the need and the effort to
obtain new labeled samples by exploiting data that are available in related
areas, and using these further across similar fields. This has given rise to a
new machine learning framework known as transfer learning: a learning setting
inspired by the capability of a human being to extrapolate knowledge across
tasks to learn more efficiently. Despite a large amount of different transfer
learning scenarios, the main objective of this survey is to provide an overview
of the state-of-the-art theoretical results in a specific, and arguably the
most popular, sub-field of transfer learning, called domain adaptation. In this
sub-field, the data distribution is assumed to change across the training and
the test data, while the learning task remains the same. We provide a first
up-to-date description of existing results related to domain adaptation problem
that cover learning bounds based on different statistical learning frameworks
Hand in hand: Public endorsement of climate change mitigation and adaptation
This research investigated how an individual's endorsements of mitigation and adaptation relate to each other, and how well each of these can be accounted for by relevant social psychological factors. Based on survey data from two European convenience samples (N = 616 / 309) we found that public endorsements of mitigation and adaptation are strongly associated: Someone who is willing to reduce greenhouse gas emissions (mitigation) is also willing to prepare for climate change impacts (adaptation). Moreover, people endorsed the two response strategies for similar reasons: People who believe that climate change is real and dangerous, who have positive attitudes about protecting the environment and the climate, and who perceive climate change as a risk, are willing to respond to climate change. Furthermore, distinguishing between (spatially) proximal and distant risk perceptions suggested that the idea of portraying climate change as a proximal (i.e., local) threat might indeed be effective in promoting personal actions. However, to gain endorsement of broader societal initiatives such as policy support, it seems advisable to turn to the distant risks of climate change. The notion that "localising" climate change might not be the panacea for engaging people in this domain is discussed in regard to previous theory and research
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
A review of domain adaptation without target labels
Domain adaptation has become a prominent problem setting in machine learning
and related fields. This review asks the question: how can a classifier learn
from a source domain and generalize to a target domain? We present a
categorization of approaches, divided into, what we refer to as, sample-based,
feature-based and inference-based methods. Sample-based methods focus on
weighting individual observations during training based on their importance to
the target domain. Feature-based methods revolve around on mapping, projecting
and representing features such that a source classifier performs well on the
target domain and inference-based methods incorporate adaptation into the
parameter estimation procedure, for instance through constraints on the
optimization procedure. Additionally, we review a number of conditions that
allow for formulating bounds on the cross-domain generalization error. Our
categorization highlights recurring ideas and raises questions important to
further research.Comment: 20 pages, 5 figure
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