4 citations · 5 across the 11 of their papers we have counts for
6 papers · 1 filter
Efficiency of Generalized Proportional First-Price Auctions Under Auto-bidding
Yang Cai, Vineet Gupta, Yanchen Jiang +4
Auto-bidding is now widely adopted in online advertising platforms, allowing advertisers to specify high-level campaign objectives--such as maximizing total value subject to a retu…
LLM Bidders Preserve the Mechanism-Level Orderings of Human Bidders
Anand Shah, Kehang Zhu, Yanchen Jiang +4
Training on vast amounts of human-generated data has motivated growing interest in using large language models (LLMs) to simulate human behavior. We ask which features of human beh…
BundleFlow: Deep Menus for Combinatorial Auctions by Diffusion-Based Optimization
Tonghan Wang, Yanchen Jiang, David C. Parkes
Differentiable economics -- the use of deep learning for auction design -- has driven progress in the automated design of multi-item auctions with additive or unit-demand valuation…
Charting the Shapes of Stories with Game Theory
Constantinos Daskalakis, Ian Gemp, Yanchen Jiang +3
Stories are records of our experiences and their analysis reveals insights into the nature of being human. Successful analyses are often interdisciplinary, leveraging mathematical…
GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning
Tonghan Wang, Yanchen Jiang, David C. Parkes
Automated mechanism design (AMD) uses computational methods for mechanism design. Differentiable economics is a form of AMD that uses deep learning to learn mechanism designs and h…
Data Market Design through Deep Learning
Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes
The data market design problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expected revenue to the information seller,…