4 papers
Learning Truthful Mechanisms without Discretization
Yunxuan Ma, Siqiang Wang, Zhijian Duan +2
This paper introduces TEDI (Truthful, Expressive, and Dimension-Insensitive approach), a discretization-free algorithm to learn truthful and utility-maximizing mechanisms. Existing…
Large-Scale Contextual Market Equilibrium Computation through Deep Learning
Yunxuan Ma, Yide Bian, Hao Xu +5
Market equilibrium is one of the most fundamental solution concepts in economics and social optimization analysis. Existing works on market equilibrium computation primarily focus…
An Adaptable Budget Planner for Enhancing Budget-Constrained Auto-Bidding in Online Advertising
Zhijian Duan, Yusen Huo, Tianyu Wang +6
In online advertising, advertisers commonly utilize auto-bidding services to bid for impression opportunities. A typical objective of the auto-bidder is to optimize the advertiser'…
Automated Deterministic Auction Design with Objective Decomposition
Zhijian Duan, Haoran Sun, Yichong Xia +6
Identifying high-revenue mechanisms that are both dominant strategy incentive compatible (DSIC) and individually rational (IR) is a fundamental challenge in auction design. While t…