A betting loss for bounded regression yields a variance-adaptive generalization bound, improving on first-order bounds that scale with the worst-case proxy f(1-f).
On least squares and linear combination of observations
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Second-Order Bounds for [0,1]-Valued Regression via Betting Loss
A betting loss for bounded regression yields a variance-adaptive generalization bound, improving on first-order bounds that scale with the worst-case proxy f(1-f).