Recursive Inversion Models for Permutations

Part of Advances in Neural Information Processing Systems 27 (NIPS 2014)

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Christopher Meek, Marina Meila


We develop a new exponential family probabilistic model for permutations that can capture hierarchical structure, and that has the well known Mallows and generalized Mallows models as subclasses. We describe how one can do parameter estimation and propose an approach to structure search for this class of models. We provide experimental evidence that this added flexibility both improves predictive performance and enables a deeper understanding of collections of permutations.