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
Offline reinforcement learning (RL) aims to learn optimal policies from static datasets while enhancing generalization to out-of-distribution (OOD) data. To mitigate overfitting to suboptimal behaviors in offline datasets, existing methods often relax constraints on policy and data or extract informative patterns throu...
Da Wang, Yi Ma, Ting Guo et al.· Neural Information Processin...· 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
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