ASAP integrates an LLM agent over a pool of HPO tools and adds system-level optimizations (prefix-stable prompts, speculation parallelism, Self-Tuner) to improve end-to-end wall-clock performance on diverse HPO tasks.
2109.09831 , archivePrefix=
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
Experimental comparison of 15 HPO and NAS algorithms for automated feature preprocessing on 45 tabular datasets finds evolution-based methods and random search as top performers.
QuickScope uses modified COUP Bayesian optimization to find truly difficult questions in dynamic LLM benchmarks more sample-efficiently than baselines while cutting false positives.
citing papers explorer
-
ASAP: Agent-System Co-Design for Wall-Clock-Centered Auto HPO Research for ML Experiments
ASAP integrates an LLM agent over a pool of HPO tools and adds system-level optimizations (prefix-stable prompts, speculation parallelism, Self-Tuner) to improve end-to-end wall-clock performance on diverse HPO tasks.
-
Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data
Experimental comparison of 15 HPO and NAS algorithms for automated feature preprocessing on 45 tabular datasets finds evolution-based methods and random search as top performers.