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 optimization-in-the-loop benchmarks.
E USE OFLARGELANGUAGEMODELS Large language models (LLMs) were used in the preparation of this paper
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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 optimization-in-the-loop benchmarks.