Proposes an optimal blending framework for proxy and north star metrics in online A/B testing that adjusts decision weights based on statistical power and proxy quality.
In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
2 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
EEG-MoCE assigns each modality a learnable-curvature hyperbolic expert and fuses them with curvature-aware weights, claiming SOTA on emotion, sleep, and cognitive EEG tasks.
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Blending Proxy Metrics with a North Star
Proposes an optimal blending framework for proxy and north star metrics in online A/B testing that adjusts decision weights based on statistical power and proxy quality.
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EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts
EEG-MoCE assigns each modality a learnable-curvature hyperbolic expert and fuses them with curvature-aware weights, claiming SOTA on emotion, sleep, and cognitive EEG tasks.