Unverified50% confidenceFactExact time
The paper proves that this bound is minimax optimal in the sample size and the expected regeneration time.
1
Sources
50%
Confidence
Long-term
Relevance
10/22/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedFew-shot forecasting allows lightweight adaptation using a small number of historical samples.72% similarUnverifiedThe minimax-optimal exponent describes the best error convergence rate that any estimation method can achieve across all possible data-generating distributions and in the worst case.70% similarUnverifiedAssuming the data follows a Gaussian distribution, maximizing the likelihood is equivalent to minimizing the mean squared error.70% similarVerifiedUsing a lightweight verification model or classifier much smaller than the main model to judge output credibility can keep VRAM overhead to a minimum.69% similarUnverifiedSetting an absolute failure count ceiling for small-sample slices is more robust than setting a percentage threshold and is insensitive to sample size68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1083941API
curl https://kongchang.com/api/v1/knowledge/claims/1083941MCP
get_claim(id=1083941)