4 citations · 4 across the 5 of their papers we have counts for
6 papers
MFTune: An Efficient Multi-fidelity Framework for Spark SQL Configuration Tuning
Beicheng Xu, Lingching Tung, Yuchen Wang +2
Apache Spark SQL is a cornerstone of modern big data analytics.However,optimizing Spark SQL performance is challenging due to its vast configuration space and the prohibitive cost…
CoFEH: LLM-driven Feature Engineering Empowered by Collaborative Bayesian Hyperparameter Optimization
Beicheng Xu, Keyao Ding, Wei Liu +2
Feature Engineering (FE) is pivotal in automated machine learning (AutoML) but remains a bottleneck for traditional methods, which operate within rigid search spaces and lack domai…
Tree-Structured Synergy of Large Language Models and Bayesian Optimization for Efficient CASH
Beicheng Xu, Weitong Qian, Lingching Tung +2
To lower the expertise barrier in machine learning, the AutoML community has focused on the CASH problem, which jointly automates algorithm selection and hyperparameter tuning. Whi…
PSEO: Optimizing Post-hoc Stacking Ensemble Through Hyperparameter Tuning
Beicheng Xu, Wei Liu, Keyao Ding +2
The Combined Algorithm Selection and Hyperparameter Optimization (CASH) problem is fundamental in Automated Machine Learning (AutoML). Inspired by the success of ensemble learning,…
DivBO: Diversity-aware CASH for Ensemble Learning
Yu Shen, Yupeng Lu, Yang Li +3
The Combined Algorithm Selection and Hyperparameters optimization (CASH) problem is one of the fundamental problems in Automated Machine Learning (AutoML). Motivated by the success…
Rover: An online Spark SQL tuning service via generalized transfer learning
Yu Shen, Xinyuyang Ren, Yupeng Lu +6
Distributed data analytic engines like Spark are common choices to process massive data in industry. However, the performance of Spark SQL highly depends on the choice of configura…