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| Format: | Preprint |
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2020
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| Online Access: | https://arxiv.org/abs/2009.10303 |
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| _version_ | 1866911783141244928 |
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| author | Baptista, Ricardo Marzouk, Youssef Zahm, Olivier |
| author_facet | Baptista, Ricardo Marzouk, Youssef Zahm, Olivier |
| contents | 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$\unicode{x2014}$approximations of the Knothe$\unicode{x2013}$Rosenblatt (KR) rearrangement$\unicode{x2014}$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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2009_10303 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | On the representation and learning of monotone triangular transport maps Baptista, Ricardo Marzouk, Youssef Zahm, Olivier Machine Learning Functional Analysis Computation Methodology 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$\unicode{x2014}$approximations of the Knothe$\unicode{x2013}$Rosenblatt (KR) rearrangement$\unicode{x2014}$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. |
| title | On the representation and learning of monotone triangular transport maps |
| topic | Machine Learning Functional Analysis Computation Methodology |
| url | https://arxiv.org/abs/2009.10303 |