Scaling laws hold logarithmically for model size in autoregressive jet generation, with next-token loss correlating to physical metrics via sliced Wasserstein distance, but show weaker scaling for dataset size and compute due to rapid saturation.
Refereeing the Referees: Evaluating Two- Sample Tests for Validating Generators in Precision Sciences
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Nyström approximation of MMD enables scalable two-sample testing with permutation p-values and a finite-sample power bound matching the minimax optimal separation rate.
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Neural Scaling Laws for Jet Generation
Scaling laws hold logarithmically for model size in autoregressive jet generation, with next-token loss correlating to physical metrics via sliced Wasserstein distance, but show weaker scaling for dataset size and compute due to rapid saturation.
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A Scalable Nystrom-Based Kernel Two-Sample Test with Permutations
Nyström approximation of MMD enables scalable two-sample testing with permutation p-values and a finite-sample power bound matching the minimax optimal separation rate.