@InProceedings{daume05laso,
  author =       {Hal {Daum\'e III} and Daniel Marcu},
  title =        {Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction}
  booktitle =    {International Conference on Machine Learning (ICML)},
  year =         {2005},
  address =      {Bonn, Germany},
  abstract =     {
    Mappings to structured output spaces (strings, trees, partitions,
    etc.) are typically learned using extensions of classification
    algorithms to simple graphical structures (eg., linear chains) in
    which search and parameter estimation can be performed exactly.
    Unfortunately, in many complex problems, it is rare that exact search
    or parameter estimation is tractable.  Instead of learning exact
    models and searching via heuristic means, we embrace this difficulty
    and treat the structured output problem in terms of approximate
    search.  We present a framework for learning as search optimization,
    and two parameter updates with convergence theorems and bounds.
    Empirical evidence shows that our integrated approach to learning and
    decoding can outperform exact models at smaller computational cost.
  },
  url = {http://pub.hal3.name/#daume05laso}
}