Fan Yao
Fan Yao
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Single-Agent Poisoning Attacks Suffice to Ruin Multi-Agent Learning
Fan Yao
,
Yuwei Cheng
,
Ermin Wei
,
Haifeng Xu
Strategic Filtering for Content Moderation: Free Speech or Free of Distortion?
Saba Ahmadi
,
Avrim Blum
,
Haifeng Xu
,
Fan Yao
Learning from Imperfect Human Feedback: a Tale from Corruption-Robust Dueling
Yuwei Cheng
,
Fan Yao
,
Xuefeng Liu
,
Haifeng Xu
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Source Document
Mechanism Design Through Exploration Control: Optimizing the Trade-Off Between User and Creator Engagement
On User-Generated Content (UGC) platforms, recommendation algorithms significantly impact creators' motivation to produce content as …
Fan Yao
,
Yiming Liao
,
Jingzhou Liu
,
Shaoliang Nie
,
Qifan Wang
,
Hongning Wang
Human vs Generative AI in Content Creation Competition: Symbiosis or Conflict?
The advent of generative AI (GenAI) technology produces transformative impact on the content creation landscape, offering alternative …
Fan Yao
,
Chuanhao Li
,
Denis Nekipelov
,
Hongning Wang
,
Haifeng Xu
PDF
Source Document
User Welfare Optimization in Recommender Systems with Competing Content Creators
Driven by the new economic opportunities created by the creator economy, an increasing number of content creators rely on and compete …
Fan Yao
,
Yiming Liao
,
Mingzhe Wu
,
Chuanhao Li
,
Yan Zhu
,
James Yang
,
Jingzhou Liu
,
Qifan Wang
,
Haifeng Xu
,
Hongning Wang
PDF
Source Document
Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?
The past decade has witnessed the flourishing of a new profession as media content creators, who rely on revenue streams from online …
Fan Yao
,
Chuanhao Li
,
Karthik Abinav Sankararaman
,
Yiming Liao
,
Yan Zhu
,
Qifan Wang
,
Hongning Wang
,
Haifeng Xu
PDF
Source Document
How Bad is Top- Recommendation under Competing Content Creators?
Content creators compete for exposure on recommendation platforms, and such strategic behavior leads to a dynamic shift over the …
Fan Yao
,
Chuanhao Li
,
Denis Nekipelov
,
Hongning Wang
,
Haifeng Xu
PDF
Video
Source Document
Learning from a Learning User for Optimal Recommendations
Fan Yao
,
Chuanhao Li
,
Denis Nekepalov
,
Hongning Wang
,
Haifeng Xu
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Multi-Agent Learning for Iterative Dominance Elimination: Formal Barriers and New Algorithms
Jibang Wu
,
Haifeng Xu
,
Fan Yao
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