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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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4 results for “slice-matching”

Approximation properties of slice-matching operatorsmath.NA

Paper explores properties of slice-matching operators for measure transfer.

problem Efficiently transferring measures in high dimensions.

method Examines an associated slice-matching operator with source, target measures and slicing directions.

result Establishes invariance, equivariance, Lipschitz continuity, and error bounds.

slice-matching measure transfer Wasserstein distance affine registration optimal transport

2023-10-16abs ↗pdf ↗

Convergence Rates for Distribution Matching with Sliced Optimal Transportstat.ML

Study efficient iterative method for distribution matching using sliced optimal transport.

problem Efficiently match distributions using sliced optimal transport.

method Slice-matching scheme based on sliced optimal transport, with quantitative non-asymptotic rates derived.

result Derive quantitative non-asymptotic rates for convergence to target distribution.

distribution matching sliced optimal transport slice-matching Wasserstein distance non-asymptotic rates

2026-02-11abs ↗pdf ↗

Measure transfer via stochastic slicing and matchingmath.NA

Paper proves convergence of measure transfer schemes using slicing and matching.

problem Iterative schemes for measure transfer and approximation problems.

method Slicing-and-matching procedure, stochastic gradient descent on Wasserstein space.

result Almost sure convergence proof for stochastic slicing-and-matching schemes.

measure transfer stochastic slicing matching Wasserstein space convergence proof

2023-07-11abs ↗pdf ↗

Sliced Iterative Normalizing Flowscs.LG

SINF models transform arbitrary PDFs to target PDFs using 1D slices.

problem Transforming arbitrary probability distributions to target distributions efficiently.

method Iterative Optimal Transport of 1D slices, maximizing Wasserstein distance.

result SINF models generate high-quality samples and competitive density estimates.

probability distribution normalizing flow optimal transport Wasserstein distance deep learning

2020-07-01abs ↗pdf ↗