Learning Optimal Posted Prices for a Unit-Demand Buyer
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918044236775424 |
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| author | Teng, Yifeng Wang, Yifan |
| author_facet | Teng, Yifeng Wang, Yifan |
| contents | We study the problem of learning the optimal item pricing for a unit-demand buyer with independent item values, and the learner has query access to the buyer's value distributions. We consider two common query models in the literature: the sample access model where the learner can obtain a sample of each item value, and the pricing query model where the learner can set a price for an item and obtain a binary signal on whether the sampled value of the item is greater than our proposed price. In this work, we give nearly tight sample complexity and pricing query complexity of the unit-demand pricing problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02284 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Optimal Posted Prices for a Unit-Demand Buyer Teng, Yifeng Wang, Yifan Computer Science and Game Theory Data Structures and Algorithms Machine Learning We study the problem of learning the optimal item pricing for a unit-demand buyer with independent item values, and the learner has query access to the buyer's value distributions. We consider two common query models in the literature: the sample access model where the learner can obtain a sample of each item value, and the pricing query model where the learner can set a price for an item and obtain a binary signal on whether the sampled value of the item is greater than our proposed price. In this work, we give nearly tight sample complexity and pricing query complexity of the unit-demand pricing problem. |
| title | Learning Optimal Posted Prices for a Unit-Demand Buyer |
| topic | Computer Science and Game Theory Data Structures and Algorithms Machine Learning |
| url | https://arxiv.org/abs/2506.02284 |