Highly Data Parallelizable Estimation of the Sliced-Wasserstein Distance Using Cumulative Distribution Functions
arXiv:2606.30310v1 Announce Type: cross Abstract: The Sliced Wasserstein (SW) distance has emerged as a computationally attractive alternative to the Wasserstein distance by leveraging one-dimensional optimal transport along random projections. Standard estimators of the SW distance rely on Monte Carlo averages of one-dimensional Wasserstein distances computed via quantile functions, which require sorting projected samples and access to full datasets. In this work, we introduce a new class of estimators for the Sliced Wasserstein distance based on cumulative distribution functions (CDFs) of pr
The continuous drive for more efficient and scalable machine learning algorithms, particularly in generative AI and data comparison, fuels research into optimizing computational heavy tasks.
This development proposes a more computationally attractive method for comparing probability distributions, which is fundamental to many advanced AI applications, potentially accelerating development and reducing resource requirements.
The estimation approach for Sliced Wasserstein distance shifts from sorting-based methods to CDF-based methods, offering better data parallelization and a reduced need for full dataset access.
- · AI researchers
- · Developers of generative models
- · Cloud computing providers (through increased efficiency)
- · Big data analytics companies
- · Less efficient computational methods
- · Algorithms reliant on high memory access for sorting
Improved performance and scalability of AI models that rely on comparing complex data distributions.
Faster training times and reduced computational costs for generative AI, enabling more complex architectures or broader adoption.
Enhanced ability to compare and evaluate synthetic data, potentially accelerating drug discovery, materials science, or financial modeling.
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Read at arXiv cs.LG