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A User's Guide to Sampling Strategies for Sliced Optimal Transport
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This paper serves as a user's guide to sampling strategies for sliced optimal transport. We provide reminders and additional regularity results on the Sliced Wasserstein distance. We detail the construction methods, generation time complexity, theoretical guarantees, and conditions for each strategy. Additionally, we provide insights into their suitability for sliced optimal transport in theory. Extensive experiments on both simulated and real-world data offer a representative comparison of the strategies, culminating in practical recommendations for their best usage.
Forward citations
Cited by 3 Pith papers
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Streaming Sliced Optimal Transport
Stream-SW estimates sliced Wasserstein distances from streaming samples using quantile sketches, with logarithmic memory and provable error bounds.
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Efficient Sliced Wasserstein Distance Computation via Adaptive Bayesian Optimization
Bayesian optimization, seeded with quasi-Monte Carlo directions for the hybrid variants, gives sliced Wasserstein estimates that are competitive with or slightly better than prior state of the art on three optimizatio...
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