Provably Learning of Noisy-or Networks

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.