4,109 research outputs found
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
Given datasets from multiple domains, a key challenge is to efficiently
exploit these data sources for modeling a target domain. Variants of this
problem have been studied in many contexts, such as cross-domain translation
and domain adaptation. We propose AlignFlow, a generative modeling framework
that models each domain via a normalizing flow. The use of normalizing flows
allows for a) flexibility in specifying learning objectives via adversarial
training, maximum likelihood estimation, or a hybrid of the two methods; and b)
learning and exact inference of a shared representation in the latent space of
the generative model. We derive a uniform set of conditions under which
AlignFlow is marginally-consistent for the different learning objectives.
Furthermore, we show that AlignFlow guarantees exact cycle consistency in
mapping datapoints from a source domain to target and back to the source
domain. Empirically, AlignFlow outperforms relevant baselines on image-to-image
translation and unsupervised domain adaptation and can be used to
simultaneously interpolate across the various domains using the learned
representation.Comment: AAAI 202
Towards Efficient Data Valuation Based on the Shapley Value
"How much is my data worth?" is an increasingly common question posed by
organizations and individuals alike. An answer to this question could allow,
for instance, fairly distributing profits among multiple data contributors and
determining prospective compensation when data breaches happen. In this paper,
we study the problem of data valuation by utilizing the Shapley value, a
popular notion of value which originated in coopoerative game theory. The
Shapley value defines a unique payoff scheme that satisfies many desiderata for
the notion of data value. However, the Shapley value often requires exponential
time to compute. To meet this challenge, we propose a repertoire of efficient
algorithms for approximating the Shapley value. We also demonstrate the value
of each training instance for various benchmark datasets
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