Research map / Bandits and online learning / Online convex optimization and games
Learning Optimal Reserve Price against Non-myopic Bidders
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use online pricing with two-fold tree aggregation under the large-market or impatient-bidder assumption | Single-bidder repeated auctions · Single-bidder repeated auctions, full information | regret: \mathcal{O}(\alpha T) | Helped |
| Use Extended FTPL with unbiased estimated gains and tree aggregation | Single-bidder posted pricing · Single-bidder posted pricing, bandit setting | regret: \mathcal{O}(\alpha T) | Helped |
| Use approximate Vickrey with an anonymous reserve and jointly private allocation | Multi-bidder repeated auctions · Multi-bidder repeated auctions | regret against the best fixed reserve price: \le \alpha m T | Helped |
Closest papers by meaning
- Reserve Pricing in Repeated Second-Price Auctions with Strategic Bidders (ICML 2020 · similarity 0.92)
- Online Second Price Auction with Semi-Bandit Feedback under the Non-Stationary Setting (AAAI 2020 · similarity 0.91)
- Learning Safe Strategies for Value Maximizing Buyers in Uniform Price Auctions (ICML 2025 · similarity 0.90)
- Comparing Uniform Price and Discriminatory Multi-Unit Auctions through Regret Minimization (NeurIPS 2025 · similarity 0.89)
- Improved learning rates in multi-unit uniform price auctions (NeurIPS 2024 · similarity 0.89)
- Randomized Truthful Auctions with Learning Agents (NeurIPS 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.