Recovering Structured Probability Matrices

21 Feb 2016Qingqing HuangSham M. KakadeWeihao KongGregory Valiant

We consider the problem of accurately recovering a matrix B of size M by M , which represents a probability distribution over M2 outcomes, given access to an observed matrix of "counts" generated by taking independent samples from the distribution B. How can structural properties of the underlying matrix B be leveraged to yield computationally efficient and information theoretically optimal reconstruction algorithms?.. (read more)

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