Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Semi-Parametric Dynamic Contextual Pricing
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DEEP-C with Rounds instead of an oracle with known parameters and residual distribution | Contextual pricing · Dynamic contextual pricing | expected regret: Õ(√n) regret | Helped |
| Use DEEP-C rather than Decoupled DEEP-C (ablation) | Dynamic contextual pricing, d=2 · d=2, n=10,000 | 98th-percentile regret: 13% lower | Helped |
| Use Sparse DEEP-C rather than Decoupled DEEP-C (ablation) | Dynamic contextual pricing, d=2 · d=2, n=10,000 | 98th-percentile regret: 24% lower | Helped |
| Use Sparse DEEP-C rather than Decoupled DEEP-C (ablation) | Dynamic contextual pricing, d=100 · d=100 | 95th-percentile regret: 33% lower | Helped |
| Increase the confidence parameter γ (ablation) | DEEP-C variants · Simulations, d=2 and d=100 | expected regret | Hurt |
| Increase the confidence parameter γ (ablation) | DEEP-C variants · Simulations, d=2 and d=100 | regret variability | Helped |
| Decouple estimation of θ₀ from estimation of z* and revenue maximization (ablation) | Decoupled DEEP-C · Dynamic contextual pricing | expected regret rate: Õ(n^{2/3}) regret | Hurt |
Closest papers by meaning
- Improved Algorithms for Contextual Dynamic Pricing (NeurIPS 2024 · similarity 0.92)
- Contextual Dynamic Pricing with Heterogeneous Buyers (NeurIPS 2025 · similarity 0.90)
- Pricing with Contextual Elasticity and Heteroscedastic Valuation (ICML 2024 · similarity 0.90)
- Transfer Learning for Nonparametric Contextual Dynamic Pricing (ICML 2025 · similarity 0.89)
- A Parametric Contextual Online Learning Theory of Brokerage (ICML 2025 · similarity 0.89)
- Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand (NeurIPS 2026 · similarity 0.89)
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