Search Results for author: Huaijun Jiang

Found 8 papers, 3 papers with code

Towards General and Efficient Online Tuning for Spark

no code implementations5 Sep 2023 Yang Li, Huaijun Jiang, Yu Shen, Yide Fang, Xiaofeng Yang, Danqing Huang, Xinyi Zhang, Wentao Zhang, Ce Zhang, Peng Chen, Bin Cui

The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance.

Bayesian Optimization Meta-Learning

OpenBox: A Python Toolkit for Generalized Black-box Optimization

1 code implementation26 Apr 2023 Huaijun Jiang, Yu Shen, Yang Li, Beicheng Xu, Sixian Du, Wentao Zhang, Ce Zhang, Bin Cui

Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, experimental design, and database knob tuning.

Experimental Design

Rover: An online Spark SQL tuning service via generalized transfer learning

no code implementations8 Feb 2023 Yu Shen, Xinyuyang Ren, Yupeng Lu, Huaijun Jiang, Huanyong Xu, Di Peng, Yang Li, Wentao Zhang, Bin Cui

When applying transfer learning to accelerate the tuning process, we notice two domain-specific challenges: 1) most previous work focus on transferring tuning history, while expert knowledge from Spark engineers is of great potential to improve the tuning performance but is not well studied so far; 2) history tasks should be carefully utilized, where using dissimilar ones lead to a deteriorated performance in production.

Bayesian Optimization Transfer Learning

Transfer Learning based Search Space Design for Hyperparameter Tuning

no code implementations6 Jun 2022 Yang Li, Yu Shen, Huaijun Jiang, Tianyi Bai, Wentao Zhang, Ce Zhang, Bin Cui

The extensive experiments show that our approach considerably boosts BO by designing a promising and compact search space instead of using the entire space, and outperforms the state-of-the-arts on a wide range of benchmarks, including machine learning and deep learning tuning tasks, and neural architecture search.

Bayesian Optimization BIG-bench Machine Learning +2

TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning

no code implementations6 Jun 2022 Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Zhi Yang, Ce Zhang, Bin Cui

With the extensive applications of machine learning models, automatic hyperparameter optimization (HPO) has become increasingly important.

Hyperparameter Optimization Neural Architecture Search +2

Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale

no code implementations18 Jan 2022 Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang, Jixiang Li, Ji Liu, Ce Zhang, Bin Cui

The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the scalability of state-of-the-arts starts to become a crucial bottleneck.

Scheduling

Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup

1 code implementation31 Oct 2021 Huaijun Jiang, Yu Shen, Yang Li

In this paper, we describe our method for tackling the automated hyperparameter optimization challenge in QQ Browser 2021 AI Algorithm Competiton (ACM CIKM 2021 AnalyticCup Track 2).

Bayesian Optimization Hyperparameter Optimization

OpenBox: A Generalized Black-box Optimization Service

6 code implementations1 Jun 2021 Yang Li, Yu Shen, Wentao Zhang, Yuanwei Chen, Huaijun Jiang, Mingchao Liu, Jiawei Jiang, Jinyang Gao, Wentao Wu, Zhi Yang, Ce Zhang, Bin Cui

Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.

Experimental Design Transfer Learning

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