Unverified50% confidenceFactExact time
SelectPFN's advantages are likewise maintained on real covariate matrices, and the detection rate of variables with only interaction effects rises monotonically as their prior weight increases.
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50%
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Long-term
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10/16/2026
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UnverifiedThe Glivenko–Cantelli theorem guarantees that as the sample size increases, the empirical distribution function converges uniformly to the true distribution function.65% similarUnverifiedSelectPFN is a model based on prior-data fitted networks (PFN) that can jointly perform Bayesian variable selection and prediction and is designed specifically for sparse nonlinear regression tasks; in a single forward pass it not only gives prediction results but also returns the posterior inclusion probability of each variable.65% similarUnverifiedIncremental recalc precisely tracks dependencies by building a cell dependency graph, recalculating only the minimum affected subset.64% similarUnverifiedIdentifiability of a causal effect means that the interventional distribution P(Y|do(X)) can be uniquely determined from purely observational data, holds for all parameter assignments compatible with the graph structure, is independent of specific numerical values, and depends only on the graph structure.64% similarUnverified一致性正则化的基本假设是对同一输入样本施加不同扰动时模型输出应保持一致63% similar
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