Contextual Online Pricing with (Biased) Offline Data
Yixuan Zhang, Department of Industrial & Systems Engineering, University of Wisconsin-Madison, yzhang2554@wisc.edu, "3026 Ruihao Zhu, SC Johnson College of Business, Cornell University, ruihao.zhu@cornell.edu, "3026 Qiaomin Xie, Department of Industrial & Systems Engineering, University of Wisconsin-Madison, qiaomin.xie@wisc.edu
–Neural Information Processing Systems
We study contextual online pricing with biased offline data. For the scalar price elasticity case, we identify the instance-dependent quantity δ2 that measures how far the offline data lies from the (unknown) online optimum. We show that the time length T, bias bound V, size N and dispersion λmin(ˆΣ) of the offline data, and δ2 jointly determine the statistical complexity.
Neural Information Processing Systems
Jun-23-2026, 05:34:40 GMT
- Country:
- North America > United States (0.46)
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- Research Report > Experimental Study (1.00)
- Overview (0.67)
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