Research map / Learning theory and algorithms / Submodular and game algorithms
Utility Maximizer or Value Maximizer: Mechanism Design for Mixed Bidders in Online Advertising
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine VCG-style and GSP-style payments while allocating slots by bid value | Mixed-bidder auctions with public classes (MPU) · Mixed bidders, public classes | liquid social welfare | Helped |
| Use slot-based prices and allocate VMs first, then assign UMs to their best-utility slots | Mixed-bidder auctions with private classes (MPR) · Mixed bidders, private classes | approximation ratio: 2 | Helped |
| Require incentive compatibility, individual rationality, and robustness | Mechanisms for mixed bidders · Mixed bidders, private classes | approximation ratio lower bound: \frac{5}{4} | Hurt |
Closest papers by meaning
- Optimal Auction Design for Mixed Bidders (AAAI 2025 · similarity 0.92)
- Robust Auction Design in the Auto-bidding World (NeurIPS 2021 · similarity 0.90)
- Maximizing the Success Probability of Policy Allocations in Online Systems (AAAI 2024 · similarity 0.88)
- Simultaneous Optimization of Bid Shading and Internal Auction for Demand-Side Platforms (AAAI 2024 · similarity 0.87)
- No-Regret Online Autobidding Algorithms in First-price Auctions (NeurIPS 2025 · similarity 0.87)
- LLM-Auction: Generative Auction towards LLM-Native Advertising (NeurIPS 2026 · similarity 0.86)
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