PACE (Pre-execution Admission via Code Embeddings), an online-adaptive admission control framework that improves budgeted sample efficiency by estimating candidate utility prior to execution from within-run execution history, without training a separate offline predictor.
Gangyi Zhao, He-Bin Liang, Hongyao Tang et al.· Proceedings of the 32nd ACM...· 0 citations
Large Language Model (LLM)-driven AutoML agents have shown strong capabilities in constructing end-to-end machine learning pipelines. However, their effectiveness is limited by costly execution-based feedback, which can make the search for high-quality solutions inefficient under restricted computational budgets. We pr...
Gangyi Zhao, Hebin Liang, Hongyao Tang et al.· Proceedings of the 32nd ACM...· 0 citations
OmniMol is presented, a framework using hypergraphs to improve predictions of molecular properties, addressing challenges of imperfect data annotation and enhancing model explainability, and achieves state-of-the-art performance in properties prediction.
