Transductive Adaptation of Black Box Predictions
Stéphane Clinchant, Gabriela Csurka, Boris Chidlovskii
Access to data is critical to any machine
learning component aimed at training an
accurate predictive model. In reality, data
is often a subject of technical and legal
constraints. Data may contain sensitive
topics and data owners are often reluctant
to share them. Instead of access to
data, they make available decision making
procedures to enable predictions on
new data. Under the black box classifier
constraint, we build an effective domain
adaptation technique which adapts classifier
predictions in a transductive setting.
We run experiments on text categorization
datasets and show that significant gains
can be achieved, especially in the unsupervised
case where no labels are available in the target domain.
ACL, Berlin, Germany, August 7-12, 2016.