Spring 2017

Provably Learning of Noisy-or Networks

Thursday, Mar. 30, 2017 2:55 pm3:35 pm PDT

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In this talk we discuss recent works on learning the single-layer noisy or network, which is a textbook example of a Bayes net, and used for example in the classic QMR-DT software for diagnosing which disease(s) a patient may have by observing the symptoms he/she exhibits. These networks are highly non-linear, as a result previous works on matrix/tensor decomposition cannot be applied directly. In this talk we show matrix/tensor decomposition techniques can still be adapted to give strong theoretical guarantees even for these nonlinear models.

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