Citation
Stefano Ermon. "Probabilistic Inference by Hashing and Optimization". Talk or presentation, 25, January, 2016.

Abstract
Statistical inference in high-dimensional probabilistic models (i.e., with many variables) is one of the central problems of statistical machine learning and stochastic decision making. To date, only a handful of distinct methods have been developed, most notably (MCMC) sampling, decomposition, and variational methods. In this talk, I will introduce a fundamentally new approach based on random projections and combinatorial optimization. Our approach provides provable guarantees on accuracy, and outperforms traditional methods in a range of domains, in particular those involving combinations of probabilistic and causal dependencies (such as those coming from physical laws) among the variables. This allows for a tighter integration between inductive and deductive reasoning, and offers a range of new modeling opportunities.

Electronic downloads

• seminar.2016-01-25.Stefano_Ermon.pdf · application/pdf · 2920 kbytes

• HTML

  Stefano Ermon. <a
  href="http://chess.eecs.berkeley.edu/pubs/1168.html"
  ><i>Probabilistic Inference by Hashing and
  Optimization</i></a>, Talk or presentation,  25,
  January, 2016.

• Plain text

  Stefano Ermon. "Probabilistic Inference by Hashing and
  Optimization". Talk or presentation,  25, January, 2016.

• BibTeX

  @presentation{Ermon16_ProbabilisticInferenceByHashingOptimization,
      author = {Stefano Ermon},
      title = {Probabilistic Inference by Hashing and Optimization},
      day = {25},
      month = {January},
      year = {2016},
      abstract = {Statistical inference in high-dimensional
                probabilistic models (i.e., with many variables)
                is one of the central problems of statistical
                machine learning and stochastic decision making.
                To date, only a handful of distinct methods have
                been developed, most notably (MCMC) sampling,
                decomposition, and variational methods. In this
                talk, I will introduce a fundamentally new
                approach based on random projections and
                combinatorial optimization. Our approach provides
                provable guarantees on accuracy, and outperforms
                traditional methods in a range of domains, in
                particular those involving combinations of
                probabilistic and causal dependencies (such as
                those coming from physical laws) among the
                variables. This allows for a tighter integration
                between inductive and deductive reasoning, and
                offers a range of new modeling opportunities.},
      URL = {http://chess.eecs.berkeley.edu/pubs/1168.html}
  }
  

Posted by Sadigh Dorsa on 17 Feb 2016.
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