@InProceedings{daume10manifold,
  author =       {Arvind Agarwal and Samuel Gerber and Hal {Daum\'e III}},
  title =        {Learning Multiple Tasks using Manifold Regularization},
  booktitle =    {Proceedings of the Conference on Neural Information Processing Systems (NIPS)},
  year =         {2010},
  address =      {Vancouver, Canada},
  abstract =     {
    We present a novel method for multitask learning (MTL) based on
    manifold regularization.  We assume that all task parameters lie on a
    manifold which is the generalization of the assumption made in the
    existing literature i.e., task parameters share a common linear
    subspace. The proposed method uses the projection distance from the
    manifold to regularize the task parameters. The manifold structure and
    the task parameters are learned using an alternating optimization
    framework.  When the manifold structure is fixed, our method
    decomposes into learning independent tasks, making it appealing for
    learning new tasks. An approximation of the manifold regularization
    scheme is presented that preserves the convexity of the single task
    learning problem, and makes the proposed MTL framework efficient and
    easy to implement. We show the efficacy of our method on several
    datasets.
  },
  keywords = {ml da},
  tagline = {We approach multitask learning by assuming that all task parameters lie on an unkown manifold; we use a varient of manifold regularization to ensure convexity of the resulting single task learning problems.},
  url = {http://pub.hal3.name/#daume10manifold}
}