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Sample Complexity of Probability Divergences under Group Symmetry

ICML 2023 · Learning theory and algorithms › Sample complexity · approach: Sample complexity of learning distributions · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Restrict the variational test functions to group-invariant functionsWasserstein-1 estimation between finite-group-invariant distributions · Wasserstein-1estimation error bound: ((1/(|Σ|m))^(1/(d+s)) + (1/(|Σ|n))^(1/(d+s)))Helped
Restrict the variational test functions to group-invariant functionsWasserstein-1 estimation with an infinite group · Wasserstein-1estimation error bound: ((vol(X₀)/m)^(1/(d*+s)) + (vol(X₀)/n)^(1/(d*+s)))Helped
Restrict the variational test functions to group-invariant functionsLipschitz-regularized α-divergence estimation between finite-group-invariant distributions · Lipschitz-regularized α-divergenceestimation error bound: ((1/(|Σ|m))^(1/(d+s)) + (1/(|Σ|n))^(1/(d+s)))Helped
Restrict the variational test functions to group-invariant functionsLipschitz-regularized α-divergence estimation with an infinite group · Lipschitz-regularized α-divergenceestimation error bound: ((vol(X₀)/m)^(1/(d*+s)) + (vol(X₀)/n)^(1/(d*+s)))Helped
Use group-invariant test functions with a kernel satisfying the orbit-decay conditionMMD estimation between finite-group-invariant distributions · MMDestimation error bound: C_{Σ,k}=√((1+c_{Σ,k}(|Σ|-1))/|Σ|)Helped
Increase group size while keeping the kernel fixedMMD estimation · MMDsample-complexity improvementMixed
Adapt the Gaussian-kernel bandwidth inversely with group sizeMMD estimation · MMDsample-complexity improvement: C_{Σ,k}≈1/√|Σ|Helped
Increase the assumed group size from 1 to 4, 16, 64, and 256W^Σ empirical estimator · Wasserstein-1, [0,1)adjacent-group-size error ratio: 4Helped
Increase the assumed group size from 1 to 2 and 4W^Σ empirical estimator · Wasserstein-1, R²adjacent-group-size error ratio: √2≈1.414Helped

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