4 citations · 14 across the 11 of their papers we have counts for
11 papers
Runtime Action Interference for AI Control of AlphaStar in StarCraft II
Jaymari Chua, Chen Wang, Liming Zhu +1
A trained reinforcement learning policy does not determine the complete behavior that users encounter: deployment code still schedules, admits, suppresses, or replaces its proposed…
Three-Body Alignment: Aligning Chess Agent with Human Reasoning through Reranked Rationale
Jaymari Chua, Chen Wang, Liming Zhu +1
As reasoning agents become increasingly complex, aligning their underlying reasoning and decision-making processes with human conceptual models is a challenge for AI security and s…
Superhuman Game AI Disclosure: Expertise and Context Moderate Effects on Trust and Fairness
Jaymari Chua, Chen Wang, Lina Yao
As artificial intelligence surpasses human performance in select tasks, disclosing superhuman capabilities poses distinct challenges for fairness, accountability, and trust. Howeve…
Learning Natural Language Constraints for Safe Reinforcement Learning of Language Agents
Jaymari Chua, Chen Wang, Lina Yao
Generalizable alignment is a core challenge for deploying Large Language Models (LLMs) safely in real-world NLP applications. Current alignment methods, including Reinforcement Lea…
DrunkAgent: Stealthy Memory Corruption in LLM-Powered Recommender Agents
Shiyi Yang, Zhibo Hu, Xinshu Li +5
Large language model (LLM)-powered agents are increasingly used in recommender systems (RSs) to achieve personalized behavior modeling, where the memory mechanism plays a pivotal r…
From Data to Action: Charting A Data-Driven Path to Combat Antimicrobial Resistance
Qian Fu, Yuzhe Zhang, Yanfeng Shu +3
Antimicrobial-resistant (AMR) microbes are a growing challenge in healthcare, rendering modern medicines ineffective. AMR arises from antibiotic production and bacterial evolution,…
Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learning
Guanglin Zhou, Zhongyi Han, Shiming Chen +5
Recent studies indicate that large multimodal models (LMMs) potentially act as general-purpose assistants and are highly robust against different distributions. Despite this, domai…
Review-Incorporated Model-Agnostic Profile Injection Attacks on Recommender Systems
Shiyi Yang, Lina Yao, Chen Wang +2
Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks. Understanding attack tactics helps improve the robustness of RSs. We inten…
Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models
Yuzhe Zhang, Yipeng Zhang, Yidong Gan +2
Causal graph recovery is traditionally done using statistical estimation-based methods or based on individual's knowledge about variables of interests. They often suffer from data…
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization
Guanglin Zhou, Zhongyi Han, Shiming Chen +5
Domain Generalization (DG) endeavors to create machine learning models that excel in unseen scenarios by learning invariant features. In DG, the prevalent practice of constraining…
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
Zhongyi Han, Guanglin Zhou, Rundong He +7
In machine learning, generalization against distribution shifts -- where deployment conditions diverge from the training scenarios -- is crucial, particularly in fields like climat…