arXiv 27 Jan 2022 · Machine Learning · 5 citations (OpenAlex)
arXiv:2201.11341 · PDF · DOI · OpenAlex · Extracted main text
In feature-based dynamic pricing, a seller sets appropriate prices for a sequence of products (described by feature vectors) on the fly by learning from the binary outcomes of previous sales sessions ("Sold" if valuation $\geq$ price, and "Not Sold" otherwise). Existing works either assume noiseless linear valuation or precisely-known noise distribution, which limits the applicability of those algorithms in practice when these assumptions are hard to verify. In this work, we study two more agnostic models: (a) a "linear policy" problem where we aim at competing with the best linear pricing policy while making no assumptions on the data, and (b) a "linear noisy valuation" problem where the random valuation is linear plus an unknown and assumption-free noise. For the former model, we show a $\tilde{\Theta}(d^{\frac13}T^{\frac23})$ minimax regret up to logarithmic factors. For the latter model, we present an algorithm that achieves an $\tilde{O}(T^{\frac34})$ regret, and improve the best-known lower bound from $\Omega(T^{\frac35})$ to $\tilde{\Omega}(T^{\frac23})$. These results demonstrate that no-regret learning is possible for feature-based dynamic pricing under weak assumptions, but also reveal a disappointing fact that the seemingly richer pricing feedback is not significantly more useful than the bandit-feedback in regret reduction.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cohen, M. C., Lobel, I., and Paes Leme, R (2020) Feature-based dynamic pricing | 1.000 | 8 | 3 | 100% |
| 2 | Auer, P., Cesa-Bianchi, N., Freund, Y., and Schapire, R. E (2002) The nonstochastic multiarmed bandit problem | 1.000 | 5 | 3 | 100% |
| 3 | Wang, Y., Chen, B., and Simchi-Levi, D (2021) Multimodal dynamic pricing self | 0.928 | 5 | 4 | 80% |
| 4 | Kleinberg, R (2004) Nearly tight bounds for the continuum-armed bandit problem | 0.928 | 4 | 3 | 100% |
| 5 | Kleinberg, R. and Leighton, T (2003) The value of knowing a demand curve: Bounds on regret for online posted-price auctions | 0.874 | 7 | 2 | 100% |
| 6 | Javanmard, A. and Nazerzadeh, H (2019) Dynamic pricing in high-dimensions | 0.874 | 6 | 2 | 100% |
| 7 | Luo, Y., Sun, W. W., et al (2021) Distribution-free contextual dynamic pricing | 0.874 | 6 | 2 | 100% |
| 8 | Xu, J. and Wang, Y.-X (2021) Logarithmic regret in feature-based dynamic pricing self | 0.843 | 3 | 3 | 100% |
| 9 | Leme, R. P. and Schneider, J (2018) Contextual search via intrinsic volumes | 0.737 | 3 | 2 | 100% |
| 10 | Liu, A., Leme, R. P., and Schneider, J (2021) Optimal contextual pricing and extensions | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 26 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand | 0.920 | 9 | 6 |
| 2 | Doubly Fair Dynamic Pricing | 0.511 | 3 | 2 |
| 3 | Policy Optimization Using Semi-parametric Models for Dynamic Pricing | 0.511 | 2 | 1 |
| 4 | Pricing with Contextual Elasticity and Heteroscedastic Valuation | 0.000 | 1 | 1 |