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Tag: distance

  • Minimum Distance Summaries for Robust Neural Posterior Estimation

    Minimum Distance Summaries for Robust Neural Posterior Estimation arXiv:2602.09161v1 Announce Type: new Abstract: Simulation-based inference (SBI) enables amortized Bayesian inference by first training a neural posterior estimator (NPE) on prior-simulator pairs, typically through low-dimensional summary statistics, which can then be cheaply reused for fast inference by querying it on new test observations. Because NPE is…

  • Fast Estimation of Wasserstein Distances via Regression on Sliced Wasserstein Distances

    Fast Estimation of Wasserstein Distances via Regression on Sliced Wasserstein Distances arXiv:2509.20508v1 Announce Type: new Abstract: We address the problem of efficiently computing Wasserstein distances for multiple pairs of distributions drawn from a meta-distribution. To this end, we propose a fast estimation method based on regressing Wasserstein distance on sliced Wasserstein (SW) distances. Specifically, we…

  • Repulsive Monte Carlo on the sphere for the sliced Wasserstein distance

    Repulsive Monte Carlo on the sphere for the sliced Wasserstein distance arXiv:2509.10166v1 Announce Type: new Abstract: In this paper, we consider the problem of computing the integral of a function on the unit sphere, in any dimension, using Monte Carlo methods. Although the methods we present are general, our guiding thread is the sliced Wasserstein…

  • Mallows Model with Learned Distance Metrics: Sampling and Maximum Likelihood Estimation

    Mallows Model with Learned Distance Metrics: Sampling and Maximum Likelihood Estimation arXiv:2507.08108v1 Announce Type: new Abstract: textit{Mallows model} is a widely-used probabilistic framework for learning from ranking data, with applications ranging from recommendation systems and voting to aligning language models with human preferences~cite{chen2024mallows, kleinberg2021algorithmic, rafailov2024direct}. Under this model, observed rankings are noisy perturbations of a…

  • An in depth look at the Procrustes-Wasserstein distance: properties and barycenters

    An in depth look at the Procrustes-Wasserstein distance: properties and barycenters arXiv:2507.00894v1 Announce Type: new Abstract: Due to its invariance to rigid transformations such as rotations and reflections, Procrustes-Wasserstein (PW) was introduced in the literature as an optimal transport (OT) distance, alternative to Wasserstein and more suited to tasks such as the alignment and comparison…

  • Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flows

    Smoothed Distance Kernels for MMDs and Applications in Wasserstein Gradient Flows arXiv:2504.07820v1 Announce Type: new Abstract: Negative distance kernels $K(x,y) := – |x-y|$ were used in the definition of maximum mean discrepancies (MMDs) in statistics and lead to favorable numerical results in various applications. In particular, so-called slicing techniques for handling high-dimensional kernel summations profit…