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New framework tackles pricing and inventory with contextual, censored demand

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework to address the challenge of optimal pricing and inventory control for retailers facing fluctuating market conditions and obscured demand data. The proposed model treats demand as a linear combination of basis functions with unknown coefficients, enabling adaptive decisions that respond to contextual factors. An efficient algorithm has been designed to achieve strong regret bounds, with numerical experiments validating its effectiveness across various scenarios. AI

IMPACT This research offers a novel algorithmic approach for optimizing retail operations by better handling complex demand scenarios.

RANK_REASON The item is an academic paper published on arXiv detailing a new framework and algorithm for a specific problem in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework tackles pricing and inventory with contextual, censored demand

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The item is an academic paper published on arXiv detailing a new framework and algorithm for a specific problem in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zean Han, Zezhen Ding, Jiheng Zhang ·

    Learning to Price and Stock Under Contextual and Censored Demand

    arXiv:2609.06083v1 Announce Type: new Abstract: To make optimal joint pricing and inventory control decisions is a critical challenge for modern retailers. In practice, retailers face changing market conditions where demands are influenced by various contextual factors, while sim…

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