Ruiqi Lyu — accepted AI-conference papers

2026

Explaining Concept Shift with Interpretable Feature Attribution

Ruiqi Lyu, Alistair Turcan, Bryan Wilder

ICML 2026poster

Concept shift occurs when the distribution of labels conditioned on the features changes between domains, making even a well-tuned ML model to have learned a fundamentally incorrect representation. Identifying these shifted features provides unique insight into how one dataset differs from another, …

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2025

Predicting Language Models’ Success at Zero-Shot Probabilistic Prediction

Kevin Ren, Santiago Cortes-Gomez, Carlos Miguel Pati{\~n}o, Ananya Joshi, Ruiqi Lyu, Jingjing Tang +4

EMNLP 2025

Recent work has investigated the capabilities of large language models (LLMs) as zero-shot models for generating individual-level characteristics (e.g., to serve as risk models or augment survey datasets). However, when should a user have confidence that an LLM will provide high-quality predictions

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