arXiv Machine Learning By Maria-Florina Balcan, Tejas Pagare, Karan Singh

Learning to Price with Persuasion

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arXiv:2608. 16699v1 Announce Type: cross Abstract: Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model that simultaneously involves information and mechanism design.

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arXiv Machine Learning
Aug 18

Dynamic Pricing and Advertising with Demand Learning

arXiv:2304. 14385v4 Announce Type: replace-cross Abstract: We consider a novel pricing and advertising framework in which a seller not only sets the product price but also designs flexible advertising schemes to influence customers' valuations of the product.

By Shipra Agrawal, Yiding Feng, Wei Tang
arXiv AI
Sep 4

Data Market Design through Deep Learning

The paper tackles the data market design problem, which seeks signaling schemes that maximize revenue for an information seller. It applies deep learning to learn these schemes, addressing both obedience and incentive constraints, and demonstrates that the framework can replicate known theoretical solutions, extend to more complex scenarios, and suggest new optimal designs. The study builds on prior auction‑design work and introduces a novel approach for revenue‑optimal data markets.

By Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes