Search Machine Learning Repository: @inproceedings{icml2014c2_bachman14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Sample-based approximate regularization},
    Url = {http://jmlr.org/proceedings/papers/v32/bachman14.pdf},
    Abstract = {We introduce a method for regularizing linearly parameterized functions using general derivative-based penalties, which relies on sampling as well as finite-difference approximations of the relevant derivatives. We call this approach sample-based approximate regularization (SAR). We provide theoretical guarantees on the fidelity of such regularizers, compared to those they approximate, and prove that the approximations converge efficiently. We also examine the empirical performance of SAR on several datasets.},
    Author = {Philip Bachman and Amir-massoud Farahmand and Doina Precup},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {1926-1934}
   }