Unverified50% confidenceFactExact time
For any finite population, the mean-subtracted ES update is exactly equal to the centroid displacement multiplied by a scalar factor that depends on the mean reward.
1
Sources
50%
Confidence
Long-term
Relevance
10/27/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIn standard ES, subtracting the mean reward from the rewards (mean subtraction) reduces variance; from the centroid perspective, it switches the displacement reference point from the model's current position to the sampled population centroid, with direction and relative magnitude unchanged.78% similarUnverifiedWhen the number of match samples is sufficiently large, Elo ratings converge to the maximum likelihood estimate.67% similarUnverifiedA study decomposing ensemble gains points out that reported "ensemble gain" numbers can flip sign depending on the choice of baseline (+9.67pp vs -8.33pp on different datasets), and that many so-called ensemble gains are merely artifacts of comparison choices.67% similarUnverifiedThe effective sample size (ESS) metric cannot reflect the loss of lineage diversity, because multinomial resampling resets ESS to its maximum value, but the number of distinct lineages may have already dropped substantially.64% similarUnverifiedUnder a margin condition with exponent β, using buffered ERM calibrated with a margin buffer achieves excess risk on the order of Q^{-1/2}+N^{-β/2} (ignoring logarithmic factors).62% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1103110API
curl https://kongchang.com/api/v1/knowledge/claims/1103110MCP
get_claim(id=1103110)