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On the representation and learning of monotone triangular transport maps

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Abstract

Transportation of measure provides a versatile approach for modeling complex probability distributions, with applications in density estimation, Bayesian inference, generative modeling, and beyond. Monotone triangular transport maps---approximations of the Knothe--Rosenblatt (KR) rearrangement---are a canonical choice for these tasks. Yet the representation and parameterization of such maps have a significant impact on their generality and expressiveness, and on properties of the optimization problem that arises in learning a map from data (e.g., via maximum likelihood estimation). We present a general framework for representing monotone triangular maps via invertible transformations of smooth functions. We establish conditions on the transformation such that the associated infinite-dimensional minimization problem has no spurious local minima, i.e., all local minima are global minima; and we show for target distributions satisfying certain tail conditions that the unique global minimizer corresponds to the KR map. Given a sample from the target, we then propose an adaptive algorithm that estimates a sparse semi-parametric approximation of the underlying KR map. We demonstrate how this framework can be applied to joint and conditional density estimation, likelihood-free inference, and structure learning of directed graphical models, with stable generalization performance across a range of sample sizes.
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Dates and versions

hal-03060198 , version 1 (30-01-2023)

Identifiers

  • HAL Id : hal-03060198 , version 1

Cite

Ricardo Baptista, Youssef Marzouk, Olivier Zahm. On the representation and learning of monotone triangular transport maps. 2023. ⟨hal-03060198⟩
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