Optimal transport

…dynamical systems, model reduction, optimal transport, network science, tensor methods and related applications. Minitutorial and minisymposium proposals are due 26 October 2026 and contributed presentation abstracts due 23 November 2026.

Optimal transport supports clustering, dictionary learning and domain adaptation, but high-dimensional data often force projections that discard information. Charles Bouveyron develops an alternative for Gaussian distributions with parsimonious covariance structure and low intrinsic dimension. A low-dimensional decomposition of empirical covariance matrices gives an efficient, robust closed form for the 2-Wasserstein distance without projecting the data. An explicit Monge map uses exact covariance square roots and inverse square roots. Numerical comparisons demonstrate analytical and computati

Classical optimal transport assumes fully specified probability distributions. This talk relaxes that requirement by representing partially known marginals with belief functions. It examines joint belief functions with fixed marginals and minimizes a Choquet integral of transport cost. Preferences use an α-maxmin criterion, with α between zero and one measuring pessimism. When the cost is a ground metric, the construction gives a Choquet–Wasserstein pseudo-distance for comparing belief functions on a common space. The same framework describes games under ambiguity through a Stackelberg–Courno

…of update to Schrödinger bridges in optimal transport.

Jessica Howard presents optimal transport as a common language for neural-network ensembles, statistical field theories and exact renormalisation-group flows. Viewing a field theory as a probability distribution over functions makes transport methods useful for numerical nonperturbative calculations and analytical insights. The talk connects these ideas to particle-physics data analysis and to feature learning and training dynamics in asymptotically wide networks.

Optimal transport is a mathematical method to define a distance between probability distributions. This is particularly useful in various domains, including physics, biology, machine learning, and economics, among others. After introducing the Optimal Transport (OT) problem at finite temperature, we show how it can be formulated as a statistical physics problem. This approach allows us to derive very efficient algorithms to effectively compute the distance between two probability distributions. The a priori unrelated Schrödinger bridge (SB) problem is presented, and it is shown to be a dynamic

…spikes across pairs of epochs based on optimal transpo

…Leveraging Optimal Transport (OT) methods [5, 6], we derive an algorithm, denoted as unbalanced fused Gromov Wasserstein (UFGW), to compute whole-brain mappings between subjects with minimal anatomical priors, and provide a fast GPU-bas