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(Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy

NeurIPS 2023 · Learning theory and algorithms › Sample complexity · approach: Robust and surrogate-loss learning bounds · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use logits instead of full features for the critic (ablation)Dis² · All shifts, non-domain-adversarialMAE: 0.2807 -> 0.1504Helped
Use logits instead of full features for the critic (ablation)Dis² · All shifts, non-domain-adversarialcoverage: 1.0000 -> 0.9889No clear effect
Use logits instead of full features for the critic (ablation)Dis² · All shifts, domain-adversarialMAE: 0.1918 -> 0.0935Helped
Use logits instead of full features for the critic (ablation)Dis² · All shifts, domain-adversarialcoverage: 1.0000 -> 0.7500Hurt
Drop the concentration term (ablation)Dis² (logits) · All shifts, non-domain-adversarialMAE: 0.1504 -> 0.0829Helped
Drop the concentration term (ablation)Dis² (logits) · All shifts, non-domain-adversarialcoverage: 0.9889 -> 0.7556Hurt
Drop the concentration term (ablation)Dis² (logits) · All shifts, domain-adversarialMAE: 0.0935 -> 0.0639Helped
Drop the concentration term (ablation)Dis² (logits) · All shifts, domain-adversarialcoverage: 0.7500 -> 0.4167Hurt
Use the proposed disagreement logistic loss instead of the alternative disagreement losses (ablation)Dis² critic optimization · Across evaluated shiftsdisagreement discrepancy: + statistically significant increases; values not reported in supplied textHelped
Optimize the critic on fewer principal components (ablation)Dis² feature representation · Distribution-shift benchmarkserror-bound validityMixed
Use logits rather than progressively fewer top principal components (ablation)Dis² critic representation · Distribution-shift benchmarkserror-bound validityHelped
Add the concentration term to the sample-average estimate (ablation)Dis² without δ · All shiftscoverage: 0.7556 -> 0.9889; 0.4167 -> 0.7500Helped

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