1 citations · 1 across the 7 of their papers we have counts for
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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,…
SqueezeAttention: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal Budget
Zihao Wang, Bin Cui, Shaoduo Gan
Optimizing the Key-Value (KV) cache of the Large Language Model (LLM) has been considered critical to saving the cost of inference. Most of the existing KV-cache compression algori…