Unverified50% confidenceFactExact time
SIRS's sampling error bound shows that the extra bias introduced by the sampling algorithm depends only on the clipping range and does not grow uncontrollably with the block size or the degree of parallelism.
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10/16/2026
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UnverifiedSIRS provides an error bound: the KL divergence between the data distribution and the SIRS output distribution is bounded by a clipping term plus the model's training error.76% similarUnverifiedThe paper finds that whether using a full sampler or an early-stopped sampler, error bounds of the same order can be achieved (consistent up to logarithmic factors).69% similarUnverifiedThe study establishes non-asymptotic bounds on the conditional score estimation error, i.e., bounds that hold for finite sample sizes67% similarUnverifiedIn a benchmark with no truncation at all, correct default values instead exhibited the largest derivation gap, ruling out the explanation that truncation caused the artifact.67% similarUnverifiedThe margin of error only reflects the uncertainty introduced by sampling and cannot encompass non-sampling errors, such as question wording bias, non-response bias, coverage bias, and social desirability bias.67% similar
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