Recent Submissions

  • Automatic Exploitation of Fully Randomized Executables 

    Gadient, Austin; Ortiz, Baltazar; Barrato, Ricardo; Davis, Eli; Perkins, Jeff; e.a. (2019-06-11)
    We present Marten, a new end to end system for automatically discovering, exploiting, and combining information leakage and buffer overflow vulnerabilities to derandomize and exploit remote, fully randomized processes. ...
  • Gen: A General-Purpose Probabilistic Programming System with Programmable Inference 

    Cusumano-Towner, Marco F.; Saad, Feras A.; Lew, Alexander; Mansinghka, Vikash K. (2018-11-26)
    Probabilistic modeling and inference are central to many fields. A key challenge for wider adoption of probabilistic programming languages is designing systems that are both flexible and performant. This paper introduces ...
  • Towards Understanding Generalization via Analytical Learning Theory 

    Kawaguchi, Kenji; Benigo, Yoshua; Verma, Vikas; Kaelbling, Leslie Pack (2018-10-01)
    This paper introduces a novel measure-theoretic theory for machine learning that does not require statistical assumptions. Based on this theory, a new regularization method in deep learning is derived and shown to ...

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