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New methods advance triangular transport maps for probabilistic modeling · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new methods for learning triangular transport maps, which are crucial for probabilistic modeling tasks like density estimation and generative modeling. The first paper introduces Self-Structuring Transport Maps (SSTM) that jointly learn the map, variable ordering, and sparsity pattern, improving density estimates compared to methods that estimate structure first. The second paper presents multifidelity approaches to construct these maps when high-fidelity data is scarce, leveraging lower-fidelity data through hierarchical or corrective strategies to enhance map estimation and downstream simulation-based inference. AI

IMPACT Advances in triangular transport maps could improve density estimation, generative modeling, and simulation-based inference tasks.

RANK_REASON Two academic papers published on arXiv detailing new methods for triangular transport maps.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New methods advance triangular transport maps for probabilistic modeling · 2 sources tracked

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Two academic papers published on arXiv detailing new methods for triangular transport maps.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Morten Bl{\o}rstad, Pekka Parviainen, Berent {\AA}nund Str{\o}mnes Lunde ·

    Learning the Structure of Triangular Transport Maps

    arXiv:2609.37122v1 Announce Type: new Abstract: Triangular transport maps provide a flexible approach to sampling-based probabilistic modeling, including density estimation, generative modeling, and Bayesian inference. They transform an unknown target distribution into a simpler …

  2. arXiv stat.ML TIER_1 English(EN) · Owen Davis, Daniel Sharp, Youssef Marzouk, Gianluca Geraci ·

    Multifidelity Formulations for Triangular Transport

    arXiv:2610.00698v1 Announce Type: cross Abstract: We develop multifidelity methods for constructing triangular transport maps from samples, when high-fidelity data are scarce but lower-fidelity data are more abundant. Using this set of multifidelity data, we approximate a triangu…

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