<reponame>mlresearch/papersite
@InProceedings{germain13,
  pdf = {http://jmlr.org/proceedings/papers/v28/germain13.pdf},
  supplementary = {Supplementary:germain13-supp.pdf},
  number = {3},
  section = {cycle-3},
  title = {A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers},
  author = {<NAME> <NAME> <NAME> <NAME>},
  pages = {738-746},
  abstract = {We provide a first PAC-Bayesian analysis for domain adaptation (DA) which arises when the learning and test distributions differ. It relies on a novel distribution pseudodistance based on a disagreement averaging. Using this measure, we derive a PAC-Bayesian DA bound for the stochastic Gibbs classifier. This bound has the advantage of being directly optimizable for any hypothesis space. We specialize it to linear classifiers, and design a learning algorithm which shows interesting results on a synthetic problem and on a popular sentiment annotation task. This opens the door to tackling DA tasks by making use of all the PAC-Bayesian tools.  },
}
