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New BAFF filters mitigate training data interference in RTB A/B tests

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a Bid-Aware Filter Family (BAFF) to address training data interference in real-time bidding (RTB) A/B tests. This interference occurs when control and treatment models are trained on shared logs, leading to biased outcomes due to disagreements in ad selection and bidding prices. BAFF offers a structured approach to manage this bias by controlling tolerance to each interference channel independently, providing a spectrum between complete log-splitting and log-sharing. An online measurement protocol was proposed to evaluate data-sharing strategies against an interference-free reference model, showing that filter-based variants in live RTB deployments more closely preserve business metrics like CPC and CTR compared to traditional methods. AI

IMPACT This research could improve the accuracy and reliability of A/B testing for AI models in real-time bidding systems.

RANK_REASON The cluster contains a research paper detailing a new technical method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New BAFF filters mitigate training data interference in RTB A/B tests

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The cluster contains a research paper detailing a new technical method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jeonglyul Oh, Ikkyu Choi, Inseop Youn, Youngjae Kim ·

    BAFF: Bid-Aware Filter Family for Mitigating Training Data Interference in RTB A/B Tests

    arXiv:2609.08725v1 Announce Type: new Abstract: In online A/B tests for real-time bidding (RTB), control and treatment models are typically trained on a shared serving log that includes data generated by the counterpart model. This shared-log training biases each model's training…

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