Winston Chen

2 accepted papers

2026

Measuring Model Performance in the Presence of an Intervention

Winston Chen, Michael W. Sjoding, Jenna Wiens

AAAI 2026technical

AI models are often evaluated based on their ability to predict the outcome of interest. However, in many AI for social impact applications, the presence of an intervention that affects the outcome can bias the evaluation. Randomized controlled trials (RCTs) randomly assign interventions, allowing d

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2026

VLM-AR3L: Vision-Language Models for Absolute and Relative Rewards in Reinforcement Learning

Kuan-Chen Chen, Winston Chen, Wei-Fang Sun, Min-Chun Hu

IJCAI 2026

Designing effective reward functions remains a major challenge in reinforcement learning (RL), particularly in open-ended environments where task goals are abstract and difficult to quantify. In this work, we present VLM-AR3L, a framework that leverages Vision-Language Models (VLMs) to provide both

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