Unverified50% confidenceFactExact time
A learner estimating the loss distribution from limited data inevitably has bias, causing the estimate of CVaR to deviate from the true value, thereby weakening or even reversing the actual effect of risk aversion.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedIn emergency dispatch scenarios, the cost of false negatives is far higher than that of false positives; therefore, system designs intentionally lower the decision threshold, a practice known in machine learning as asymmetric cost-sensitive learning.78% similarVerifiedAfter geometric transformation, the transformation parameters must be recorded and the predicted boxes must be inverse-transformed to align them; ignoring this step will cause semantic misalignment in the consistency loss and harm training effectiveness76% similarUnverifiedWhen out-of-distribution (OOD) failure occurs, learned selectors may persistently ignore key evidence, resulting in answers that appear fluent but omit key facts, increasing the difficulty of troubleshooting.76% similarUnverifiedBias introduced by a single defective data point can leak into seemingly unrelated capability dimensions, causing the model to simultaneously regress across multiple independent metrics such as deceptiveness, compliance, and risk aversion.76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1012842API
curl https://kongchang.com/api/v1/knowledge/claims/1012842MCP
get_claim(id=1012842)