776 citations · 822 across the 12 of their papers we have counts for
13 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…
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'…
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…
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…
A survey on algorithms for Nash equilibria in finite normal-form games
Hanyu Li, Wenhan Huang, Zhijian Duan +4
Nash equilibrium is one of the most influential solution concepts in game theory. With the development of computer science and artificial intelligence, there is an increasing deman…
Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets
Yurong Chen, Qian Wang, Zhijian Duan +4
In online ad markets, a rising number of advertisers are employing bidding agencies to participate in ad auctions. These agencies are specialized in designing online algorithms and…
A Scalable Neural Network for DSIC Affine Maximizer Auction Design
Zhijian Duan, Haoran Sun, Yurong Chen +1
Automated auction design aims to find empirically high-revenue mechanisms through machine learning. Existing works on multi item auction scenarios can be roughly divided into Regre…
Are Equivariant Equilibrium Approximators Beneficial?
Zhijian Duan, Yunxuan Ma, Xiaotie Deng
Recently, remarkable progress has been made by approximating Nash equilibrium (NE), correlated equilibrium (CE), and coarse correlated equilibrium (CCE) through function approximat…
A Context-Integrated Transformer-Based Neural Network for Auction Design
Zhijian Duan, Jingwu Tang, Yutong Yin +4
One of the central problems in auction design is developing an incentive-compatible mechanism that maximizes the auctioneer's expected revenue. While theoretical approaches have en…
Is Nash Equilibrium Approximator Learnable?
Zhijian Duan, Wenhan Huang, Dinghuai Zhang +4
In this paper, we investigate the learnability of the function approximator that approximates Nash equilibrium (NE) for games generated from a distribution. First, we offer a gener…
Role-Wise Data Augmentation for Knowledge Distillation
Jie Fu, Xue Geng, Zhijian Duan +6
Knowledge Distillation (KD) is a common method for transferring the ``knowledge'' learned by one machine learning model (the \textit{teacher}) into another model (the \textit{stude…
Ekar: An Explainable Method for Knowledge Aware Recommendation
Weiping Song, Zhijian Duan, Ziqing Yang +3
This paper studies recommender systems with knowledge graphs, which can effectively address the problems of data sparsity and cold start. Recently, a variety of methods have been d…
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Weiping Song, Chence Shi, Zhiping Xiao +4
Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad or an item, is critical to many online applications such as online advertisin…