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Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts

ICLR 2023 · Learning theory and algorithms › Prediction and decision losses · approach: Distributionally robust optimization · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Constrain the adversary with a bitrate penalty instead of using unconstrained CVaR DROWaterbirds · Waterbirds testworst-group accuracy: 75.5 ± 0.4 -> 86.3 ± 0.3Helped
Constrain the adversary with a bitrate penalty instead of using unconstrained CVaR DROCelebA · CelebA testworst-group accuracy: 64.7 ± 0.6 -> 80.9 ± 0.4Helped
Constrain the adversary with a bitrate penalty instead of using unconstrained CVaR DROCivilComments · CivilComments testworst-group accuracy: 60.2 ± 0.8 -> 68.7 ± 0.9Helped
Use BR-DRO (L2) rather than unconstrained CVaR DROFMoW · FMoW testworst-region accuracy: 31.0 ± 0.3 -> 32.3 ± 0.2Helped
Use BR-DRO (L2) rather than ERMCamelyon17 · Camelyon17 testaverage accuracy: 70.6 ± 1.6 -> 71.2 ± 1.0No clear effect
Use BR-DRO rather than JTT when training data has random label noiseWaterbirds and CelebA · Waterbirds and CelebA with random training-label noiseworst-group accuracyHelped
Increase the VIB bitrate constraint strength (ablation)BR-DRO minority identification · Waterbirds and CelebAminority-group precisionHelped
Relax the bitrate constraint toward unconstrained CVaR DRO (ablation)BR-DRO · Waterbirds and CelebAminority-group precisionHurt
Increase adversary capacity (weaken the capacity constraint) (ablation)BR-DRO · Waterbirds and CelebAworst-group accuracyHelped
Use a sparse adversary rather than an unconstrained high-loss reweighting method (ablation)Synthetic noisy-label data · Synthetic datarobust predictor recoveryHelped

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