Search Machine Learning Repository: @inproceedings{icml2014c2_niepert14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Exchangeable Variable Models},
    Url = {http://jmlr.org/proceedings/papers/v32/niepert14.pdf},
    Abstract = {A sequence of random variables is exchangeable if its joint distribution is invariant under variable permutations. We introduce exchangeable variable models (EVMs) as a novel class of probabilistic models whose basic building blocks are partially exchangeable sequences, a generalization of exchangeable sequences. We prove that a family of tractable EVMs is optimal under zero-one loss for a large class of functions, including parity and threshold functions, and strictly subsumes existing tractable independence-based model families. Extensive experiments show that EVMs outperform state of the art classifiers such as SVMs and probabilistic models which are solely based on independence assumptions.},
    Author = {Mathias Niepert and Pedro Domingos},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {271-279}
   }