activity
20152026
most citedAn Overview of Machine Teaching

102 citations · 313 across the 24 of their papers we have counts for

collaborators

42 papers

cs.GT2026

The Game Changer Problem: Controlling Equilibria with Discrete Rewards

Brandon Han, Young Wu, Shiyun Cheng +1

We introduce the game changer problem, where an external designer modifies a game's reward matrix to make a target pure action profile the unique equilibrium, subject to the constr…

cs.GT2026

Pure Nash Equilibria under the Affine Mechanism: A Potential Game of Exaggeration

Jason Jisen Li, Young Wu, Yancheng Zhu +2

The mean mechanism is known to be non-incentive-compatible, namely, rational players are incentivized to misreport their values. Despite this game-theoretic issue, the mean mechani…

cs.LG2026

CFRecs: Counterfactual Recommendations on Real Estate User Listing Interaction Graphs

Seyedmasoud Mousavi, Ruomeng Xu, Xiaojing Zhu

Graph-structured data is ubiquitous and powerful in representing complex relationships in many online platforms. While graph neural networks (GNNs) are widely used to learn from su…

cs.AI2025

When Identity Skews Debate: Anonymization for Bias-Reduced Multi-Agent Reasoning

Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li

Multi-agent debate (MAD) aims to improve large language model (LLM) reasoning by letting multiple agents exchange answers and then aggregate their opinions. Yet recent studies reve…

cs.CL2025

Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?

Hyeong Kyu Choi, Xiaojin Zhu, Sharon Li

Multi-Agent Debate~(MAD) has emerged as a promising paradigm for improving the performance of large language models through collaborative reasoning. Despite recent advances, the ke…

cs.LG2025

A Cramér-von Mises Approach to Incentivizing Truthful Data Sharing

Alex Clinton, Thomas Zeng, Yiding Chen +2

Modern data marketplaces and data sharing consortia increasingly rely on incentive mechanisms to encourage agents to contribute data. However, schemes that reward agents based on t…