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Mingyu Huang

6 accepted papers

2026

Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs

AAAI 2026technical

The recent proliferation of large language models (LLMs) holds the potential to revolutionize healthcare, with strong capabilities in diverse medical tasks. Yet, deploying LLMs in high-stakes healthcare settings requires rigorous verification and validation to understand any potential harm. This pap

Cited by 2SourcePDFScholar
2026

Eating for a Sustainable Planet: Personalized Sustainable Diet Recommendation via Constraint-Aware Decision-Making Modeling

ICML 2026poster

A sustainable diet represents a multi-dimensional synergy among four essential pillars: nutrition adequacy, economic affordability, cultural acceptability, and environmental respect. Despite the prevalence of population-level sustainability modeling, practical implementation relies on effective indi…

Cited by 0SourceScholar
2026

Position: Genomic Model Research Must Move Beyond Anecdotal Evaluation of Interpretability Methods

ICML 2026spotlight

Advances in machine learning and computational power have unlocked the predictive potential of the human genome, yet biologists increasingly demand that these models also elucidate the underlying biological mechanisms. While interpretable machine learning (IML) techniques have been increasingly appl…

Cited by 0SourceScholar
2025

Augmenting Biological Fitness Prediction Benchmarks with Landscapes Features from GraphFLA

NeurIPS 2025spotlight

Machine learning models increasingly map biological sequence-fitness landscapes to predict mutational effects. Effective evaluation of these models requires benchmarks curated from empirical data. Despite their impressive scales, existing benchmarks lack topographical information regarding the under…

Cited by 0SourcecodeScholar
2023

Exploring Structural Similarity in Fitness Landscapes via Graph Data Mining: A Case Study on Number Partitioning Problems

IJCAI 2023poster

One of the most common problem-solving heuristics is by analogy. For a given problem, a solver can be viewed as a strategic walk on its fitness landscape. Thus if a solver works for one problem instance, we expect it will also be effective for other instances whose fitness landscapes essentially sha…

Cited by 6SourcePDFScholar