Research map / Learning theory and algorithms / Prediction and decision losses
Revenue Optimization with Approximate Bid Predictions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use RIC-h clustering instead of the DC algorithm | Reserve-price optimization on AdExchange auction data · AdExchange, four publisher-advertiser pairs | revenue: up to 30% improvement | Helped |
| Use RIC-h clustering instead of the offset algorithm | Reserve-price optimization on AdExchange auction data · AdExchange, four publisher-advertiser pairs | revenue | Helped |
| Use RIC-h clustering instead of the DC and offset algorithms | Reserve-price optimization under low noise · Synthetic bimodal scenario | revenue | Helped |
| Use RIC-h clustering instead of the DC and offset algorithms | Reserve-price optimization under low noise · Synthetic linear scenario | revenue | Hurt |
| Increase noise in the synthetic linear scenario | RIC-h, DC, and offset reserve-price algorithms · Synthetic linear scenario | revenue | No clear effect |
Closest papers by meaning
- Reserve Price Optimization for First Price Auctions in Display Advertising (ICML 2021 · similarity 0.90)
- Learning to bid in revenue-maximizing auctions (ICML 2019 · similarity 0.89)
- Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions (NeurIPS 2022 · similarity 0.89)
- Learning to Clear the Market (ICML 2019 · similarity 0.89)
- Improved Online Learning Algorithms for CTR Prediction in Ad Auctions (ICML 2023 · similarity 0.89)
- Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming (NeurIPS 2020 · similarity 0.89)
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