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Identifying Best Interventions through Online Importance Sampling

ICML 2017 · Bandits and online learning › Multi-armed bandits · approach: Pure exploration algorithms · 19 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use SRISv2 with adaptively clipped importance sampling and information sharing across interventionsBest soft-intervention identification · Flow Cytometryperformance: more than 65% improvementHelped
Use SRISv2 instead of SRISv1, reusing samples from earlier phases (ablation)Best soft-intervention identification · Flow CytometryperformanceHelped
Use SRISv2 rather than SR, which does not use information leakageBest soft-intervention identification · Synthetic experiments, low divergence and high gapsimple regretHelped
Use SRISv2 rather than CR with a static clipperBest soft-intervention identification · Synthetic experiments, high divergence and high gapsimple regretHelped
Replace adaptive clipping with a static clipper (ablation)Clipped importance sampling · Synthetic experiments, high divergence and high gapprobability of errorHurt
Use information leakage from other interventions rather than treating arms independentlyBest soft-intervention identification · Synthetic experiments, low divergence and high gapprobability of errorHelped
Use SRISv2 rather than CR with a static clipperBest soft-intervention identification · Synthetic experiments, low divergence and high gapprobability of errorNo clear effect
Reuse samples from earlier phases, clipping them using the current phase criterion (ablation)SRISv2 · Best soft-intervention identificationerror and simple regret guaranteesNo clear effect

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