102 citations · 609 across the 64 of their papers we have counts for
67 papers
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…
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…
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…
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…
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…
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…
Optimally Installing Strict Equilibria
Jeremy McMahan, Young Wu, Yudong Chen +2
In this work, we develop a reward design framework for installing a desired behavior as a strict equilibrium across standard solution concepts: dominant strategy equilibrium, Nash…
The Battling Influencers Game: Nash Equilibria Structure of a Potential Game and Implications to Value Alignment
Young Wu, Yancheng Zhu, Jin-Yi Cai +1
When multiple influencers attempt to compete for a receiver's attention, their influencing strategies must account for the presence of one another. We introduce the Battling Influe…
CHARTOM: A Visual Theory-of-Mind Benchmark for LLMs on Misleading Charts
Shubham Bharti, Shiyun Cheng, Jihyun Rho +5
We introduce CHARTOM, a visual theory-of-mind benchmark designed to evaluate multimodal large language models' capability to understand and reason about misleading data visualizati…
Inception: Efficiently Computable Misinformation Attacks on Markov Games
Jeremy McMahan, Young Wu, Yudong Chen +2
We study security threats to Markov games due to information asymmetry and misinformation. We consider an attacker player who can spread misinformation about its reward function to…
Stochastic gradient descent-based inference for dynamic network models with attractors
Hancong Pan, Xiaojing Zhu, Cantay Caliskan +4
In Coevolving Latent Space Networks with Attractors (CLSNA) models, nodes in a latent space represent social actors, and edges indicate their dynamic interactions. Attractors are a…
The Delusional Hedge Algorithm as a Model of Human Learning from Diverse Opinions
Yun-Shiuan Chuang, Jerry Zhu, Timothy T. Rogers
Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from heari…
Optimal Attack and Defense for Reinforcement Learning
Jeremy McMahan, Young Wu, Xiaojin Zhu +1
To ensure the usefulness of Reinforcement Learning (RL) in real systems, it is crucial to ensure they are robust to noise and adversarial attacks. In adversarial RL, an external at…
Optimally Teaching a Linear Behavior Cloning Agent
Shubham Kumar Bharti, Stephen Wright, Adish Singla +1
We study optimal teaching of Linear Behavior Cloning (LBC) learners. In this setup, the teacher can select which states to demonstrate to an LBC learner. The learner maintains a ve…
Learning interactions to boost human creativity with bandits and GPT-4
Ara Vartanian, Xiaoxi Sun, Yun-Shiuan Chuang +3
This paper considers how interactions with AI algorithms can boost human creative thought. We employ a psychological task that demonstrates limits on human creativity, namely seman…
Anytime-Constrained Reinforcement Learning
Jeremy McMahan, Xiaojin Zhu
We introduce and study constrained Markov Decision Processes (cMDPs) with anytime constraints. An anytime constraint requires the agent to never violate its budget at any point in…
Minimally Modifying a Markov Game to Achieve Any Nash Equilibrium and Value
Young Wu, Jeremy McMahan, Yiding Chen +3
We study the game modification problem, where a benevolent game designer or a malevolent adversary modifies the reward function of a zero-sum Markov game so that a target determini…
Dream the Impossible: Outlier Imagination with Diffusion Models
Xuefeng Du, Yiyou Sun, Xiaojin Zhu +1
Utilizing auxiliary outlier datasets to regularize the machine learning model has demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the labor…
VISER: A Tractable Solution Concept for Games with Information Asymmetry
Jeremy McMahan, Young Wu, Yudong Chen +2
Many real-world games suffer from information asymmetry: one player is only aware of their own payoffs while the other player has the full game information. Examples include the cr…
Data Poisoning to Fake a Nash Equilibrium in Markov Games
Young Wu, Jeremy McMahan, Xiaojin Zhu +1
We characterize offline data poisoning attacks on Multi-Agent Reinforcement Learning (MARL), where an attacker may change a data set in an attempt to install a (potentially fictiti…
Mechanism Design for Collaborative Normal Mean Estimation
Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy
We study collaborative normal mean estimation, where strategic agents collect i.i.d samples from a normal distribution at a cost. They all wish to estimat…
Non-Parametric Outlier Synthesis
Leitian Tao, Xuefeng Du, Xiaojin Zhu +1
Out-of-distribution (OOD) detection is indispensable for safely deploying machine learning models in the wild. One of the key challenges is that models lack supervision signals fro…
Provable Defense against Backdoor Policies in Reinforcement Learning
Shubham Kumar Bharti, Xuezhou Zhang, Adish Singla +1
We propose a provable defense mechanism against backdoor policies in reinforcement learning under subspace trigger assumption. A backdoor policy is a security threat where an adver…
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning
Young Wu, Jeremy McMahan, Xiaojin Zhu +1
In offline multi-agent reinforcement learning (MARL), agents estimate policies from a given dataset. We study reward-poisoning attacks in this setting where an exogenous attacker m…