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Poster
Message-Passing for Approximate MAP Inference with Latent Variables
Jiarong Jiang · Piyush Rai · Hal Daumé III

Tue Dec 13 08:45 AM -- 02:59 PM (PST) @ None #None

We consider a general inference setting for discrete probabilistic graphical models where we seek maximum a posteriori (MAP) estimates for a subset of the random variables (max nodes), marginalizing over the rest (sum nodes). We present a hybrid message-passing algorithm to accomplish this. The hybrid algorithm passes a mix of sum and max messages depending on the type of source node (sum or max). We derive our algorithm by showing that it falls out as the solution of a particular relaxation of a variational framework. We further show that the Expectation Maximization algorithm can be seen as an approximation to our algorithm. Experimental results on synthetic and real-world datasets, against several baselines, demonstrate the efficacy of our proposed algorithm.

Author Information

Jiarong Jiang (Two Sigma Investments LP)
Piyush Rai (Duke University)
Hal Daumé III (Univ of Maryland / Microsoft Research)

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