Hyperparameter Optimization

Cognitive Technology Team

Mar 17, 2025 · Artificial Intelligence

Leveraging Large Language Models to Optimize Traditional Machine Learning Pipelines

Large language models can assist and enhance each stage of traditional machine learning—including sample generation, data cleaning, feature engineering, model selection, hyper‑parameter tuning, and workflow automation—by generating synthetic data, refining features, selecting models, and orchestrating pipelines, though challenges such as bias, privacy, and noise remain.

Data GenerationHyperparameter OptimizationLLM

0 likes · 11 min read

Leveraging Large Language Models to Optimize Traditional Machine Learning Pipelines

Tencent Advertising Technology

Dec 27, 2024 · Artificial Intelligence

Tencent's AutoML Research for Advertising Recommendation Systems

This article outlines Tencent's AutoML research, presenting several recent papers that introduce novel neural architecture search, feature selection, pooling, embedding size, and hyper‑parameter optimization techniques to improve the efficiency, accuracy, and scalability of large‑scale advertising recommendation systems.

AutoMLEmbedding Size SearchHyperparameter Optimization

0 likes · 10 min read

Tencent's AutoML Research for Advertising Recommendation Systems

Python Programming Learning Circle

Mar 23, 2024 · Artificial Intelligence

Eight Python Libraries to Accelerate Data‑Science Workflows

This article introduces eight Python libraries—including Optuna, ITMO_FS, shap‑hypetune, PyCaret, floWeaver, Gradio, Terality, and Torch‑Handle—that streamline data‑science tasks such as hyperparameter optimization, feature selection, model building, visualization, and deployment, helping users save coding time and improve productivity.

Hyperparameter OptimizationLibrariesPython

0 likes · 12 min read

Eight Python Libraries to Accelerate Data‑Science Workflows

Didi Tech

Jan 25, 2024 · Artificial Intelligence

Ray-native XGBoost Training Platform: Architecture, Performance, and Technical Challenges

Didi’s new Ray‑native XGBoost training platform replaces the fault‑prone Spark solution with a fully Pythonic, fault‑tolerant architecture that leverages Ray’s autoscaling and gang‑scheduling, delivering 2–6× speedups, reduced failure rates, efficient sparse‑vector handling, scalable hyper‑parameter search, and improved resource utilization for large‑scale machine‑learning workloads.

Hyperparameter OptimizationRayXGBoost

0 likes · 20 min read

Ray-native XGBoost Training Platform: Architecture, Performance, and Technical Challenges

DataFunSummit

Feb 22, 2023 · Artificial Intelligence

AutoML Overview: Hyperparameter Optimization, Automatic Feature Engineering, and Neural Architecture Search on Alibaba PAI

This article introduces AutoML, explaining how it automates data cleaning, feature engineering, model selection, hyper‑parameter optimization, and neural architecture search, and showcases Alibaba PAI's implementations of HPO, AutoFE, and NAS with practical case studies and performance results.

Alibaba PAIAutoMLHyperparameter Optimization

0 likes · 15 min read

AutoML Overview: Hyperparameter Optimization, Automatic Feature Engineering, and Neural Architecture Search on Alibaba PAI

DataFunTalk

Feb 18, 2023 · Artificial Intelligence

Building the ATLAS Automated Machine Learning Platform at Du Xiaoman: Architecture, Optimization, and Practical Insights

This article details Du Xiaoman's development of the ATLAS automated machine learning platform, covering business scenarios, AI algorithm deployment challenges, the end‑to‑end production workflow, platform components such as annotation, data, training and deployment, as well as optimization techniques like AutoML, meta‑learning, NAS, and large‑scale parallelism, concluding with lessons learned and future directions.

AI deploymentAutoMLHyperparameter Optimization

0 likes · 20 min read

Building the ATLAS Automated Machine Learning Platform at Du Xiaoman: Architecture, Optimization, and Practical Insights

Python Programming Learning Circle

Feb 23, 2022 · Artificial Intelligence

A Survey of Python Libraries for Hyperparameter Optimization, Feature Selection, Model Explainability, and Rapid Machine Learning Development

This article introduces several Python libraries—including Optuna, ITMO_FS, shap‑hypertune, PyCaret, floWeaver, Gradio, Terality, and torch‑handle—that simplify hyperparameter tuning, feature selection, model explainability, visualization, and low‑code ML workflows, providing code examples and key advantages for each tool.

Hyperparameter OptimizationModel ExplainabilityPython

0 likes · 10 min read

A Survey of Python Libraries for Hyperparameter Optimization, Feature Selection, Model Explainability, and Rapid Machine Learning Development

Tencent Advertising Technology

Sep 22, 2020 · Artificial Intelligence

Automated Machine Learning: Challenges, Techniques, and the SolnML System – Q&A Highlights from the 2020 Tencent Advertising Algorithm Competition Live Series

This article summarizes the Q&A session of the 2020 Tencent Advertising Algorithm Competition live series, covering the fundamentals of automated machine learning, its key technologies, current challenges, and the features and advantages of the SolnML system, while also addressing practical concerns such as hardware support and future research directions.

AutoMLHyperparameter OptimizationSolnML

0 likes · 13 min read

Automated Machine Learning: Challenges, Techniques, and the SolnML System – Q&A Highlights from the 2020 Tencent Advertising Algorithm Competition Live Series

JD Tech Talk

Jul 6, 2020 · Artificial Intelligence

Meta‑Knowledge Transfer for Automated Machine Learning: System Architecture and Methodology

This article proposes a meta‑knowledge transfer framework for AutoML systems, detailing a four‑layer architecture, methods for collecting and updating structured model meta‑knowledge, and strategies that use this knowledge to guide hyper‑parameter search and early‑stop training, thereby improving efficiency and reducing resource consumption.

AutoMLHyperparameter OptimizationMeta-Knowledge

0 likes · 23 min read

Meta‑Knowledge Transfer for Automated Machine Learning: System Architecture and Methodology

JD Tech Talk

Apr 24, 2020 · Artificial Intelligence

Automated Machine Learning System Architecture and Hyper‑Parameter Optimization Process

This article presents a comprehensive automated machine‑learning platform that abstracts task design, hyper‑parameter search space management, optimization engines, algorithm repositories, training/evaluation engines, model repositories and monitoring panels, offering both expert‑assisted and code‑free modes to accelerate model building while reducing reliance on specialist knowledge.

AI PlatformAutoMLHyperparameter Optimization

0 likes · 17 min read

Automated Machine Learning System Architecture and Hyper‑Parameter Optimization Process

JD Tech Talk

Dec 5, 2019 · Artificial Intelligence

An Overview of Automated Machine Learning (AutoML): Definitions, Algorithms, Frameworks, and Open Challenges

This article provides a comprehensive overview of Automated Machine Learning (AutoML), covering its definition, objectives, research areas, hyperparameter optimization methods, pipeline construction, major CASH algorithms, open-source frameworks such as AutoSklearn and NNI, practical case studies, and current open research challenges.

AutoMLHyperparameter OptimizationNeural Architecture Search

0 likes · 14 min read

An Overview of Automated Machine Learning (AutoML): Definitions, Algorithms, Frameworks, and Open Challenges

DataFunTalk

Sep 11, 2019 · Artificial Intelligence

AutoML for Tabular Data: Research, Techniques, and Applications

This talk presents the research and practical deployment of AutoML for tabular data, covering background, automated feature engineering and selection, hyper‑parameter optimization, the AutoCross feature‑crossing system, case studies, and future directions, demonstrating its advantages over Google Cloud AutoML on multiple Kaggle competitions.

AutoMLHyperparameter Optimizationfeature engineering

0 likes · 14 min read

AutoML for Tabular Data: Research, Techniques, and Applications

DataFunTalk

Jun 25, 2019 · Artificial Intelligence

Applying AutoML to Recommendation Systems: Techniques, Optimizations, and Practical Insights

This article presents a comprehensive overview of applying Automated Machine Learning (AutoML) to recommendation systems, detailing methods for data preprocessing, feature engineering, model selection, hyper‑parameter optimization, and neural architecture search, and shares practical experiences and performance gains observed in real‑world deployments.

AutoMLHyperparameter OptimizationRecommendation systems

0 likes · 28 min read

Applying AutoML to Recommendation Systems: Techniques, Optimizations, and Practical Insights