Research map / Bandits and online learning / Online convex optimization and games
Multi-channel Autobidding with Budget and ROI Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| optimize only per-channel target ROIs (no budget lever) | multi-channel advertiser problem · theoretical example (2 channels, 3 auctions) | CH-OPT(I_R)/GL-OPT ratio: ratio approaches 0 as X grows | Hurt |
| optimize only per-channel budgets (budget-only lever) | multi-channel advertiser problem · theoretical | total conversion vs GL-OPT: equals GL-OPT | Helped |
| augment per-channel budget learning with UCB (SGD-UCB) under bandit feedback | per-channel budget optimization · synthetic, T=200, optimal autobidding | conversion as % of GL-OPT: more than 91% of global optimal | Helped |
| increase number of periods T from 100 to 1000 (ablation) | SGD-UCB · synthetic, optimal autobidding | conversion as % of GL-OPT and variance | Helped |
| lower channel autobidding optimality (corruption factors alpha from 1 down to 0.2) (ablation) | SGD-UCB · synthetic, T=200, normalized conversion | conversion as % of optimal normalized conversion | Mixed |
Closest papers by meaning
- Online Ad Procurement in Non-stationary Autobidding Worlds (NeurIPS 2023 · similarity 0.90)
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- LLM-Auction: Generative Auction towards LLM-Native Advertising (NeurIPS 2026 · similarity 0.86)
- Online Learning under Budget and ROI Constraints via Weak Adaptivity (ICML 2024 · similarity 0.86)
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