Research map / Bandits and online learning / Online convex optimization and games
Optimal Non-parametric Learning in Repeated Contextual Auctions with Strategic Buyer
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Penalization rounds plus isolation of context domains (PELS) instead of standard penalization | contextual repeated posted-price auctions with strategic Lipschitz buyer · theoretical worst-case strategic regret | strategic regret order | Helped |
| Original non-contextual penalization applied without context-dependent isolation (ablation) | contextual setting · theoretical analysis | gap between lower and upper bounds | Hurt |
Closest papers by meaning
- A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions (NeurIPS 2019 · similarity 0.92)
- Bisection-Based Pricing for Repeated Contextual Auctions against Strategic Buyer (ICML 2020 · similarity 0.92)
- Improved Algorithms for Contextual Dynamic Pricing (NeurIPS 2024 · similarity 0.90)
- A Parametric Contextual Online Learning Theory of Brokerage (ICML 2025 · similarity 0.88)
- Contextual Dynamic Pricing with Heterogeneous Buyers (NeurIPS 2025 · similarity 0.88)
- Learning Safe Strategies for Value Maximizing Buyers in Uniform Price Auctions (ICML 2025 · similarity 0.87)
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