Unverified50% confidenceFactExact time
The loss estimator designed in the paper combines three key components: forced exploration, conditional geometric resampling, and an adaptive learning rate with surrogate probabilities.
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11/4/2026
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UnverifiedExperimental results show that PORTAL can turn an intervention that "on the whole would lose accuracy" into a beneficial choice on selected batches.78% similarUnverifiedA 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.75% similarUnverifiedThe researchers proposed a heteroscedasticity-aware extension of quantile loss, whose core approach is to use input-dependent label smoothing as a mitigation measure.73% similarUnverified分位数回归、集成学习和贝叶斯方法三种不确定性估计路径各有取舍：分位数回归计算最省，集成学习实现简单但训练成本高，贝叶斯方法理论最优雅但收敛慢。73% similarUnverifiedConditional geometric resampling estimates the expected loss of unobserved nodes by resampling from a geometric distribution, ensures unbiasedness of the estimate conditional on the given history, and controls variance through the tail properties of the geometric distribution.73% similar
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