# Probabilistic Matrix Factorization for Automated Machine Learning

arXiv: https://arxiv.org/abs/1705.05355 · HTML: https://mlsanity.com/paper/1705.05355/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2017.

## Built on by (most cited first, 30 of 31)

- [Group-wise Reinforcement Feature Generation for Optimal and Explainable Representation Space Reconstruction](https://mlsanity.com/paper/2205.14526/) (2022)
- [Towards Green Automated Machine Learning: Status Quo and Future Directions](https://mlsanity.com/paper/2111.05850/) (2021)
- [Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective](https://mlsanity.com/paper/2306.16893/) (2023)
- [Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation](https://mlsanity.com/paper/2311.13184/) (2023)
- [How Much Automation Does a Data Scientist Want?](https://mlsanity.com/paper/2101.03970/) (2021)
- [Winning solutions and post-challenge analyses of the ChaLearn AutoDL challenge 2019](https://mlsanity.com/paper/2201.03801/) (2022)
- [Naive Automated Machine Learning](https://mlsanity.com/paper/2111.14514/) (2021)
- [Bayesian Optimization Over Iterative Learners with Structured Responses: A Budget-aware Planning Approach](https://mlsanity.com/paper/2206.12708/) (2022)
- [A Scalable AutoML Approach Based on Graph Neural Networks](https://mlsanity.com/paper/2111.00083/) (2021)
- [mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale](https://mlsanity.com/paper/2506.21550/) (2025)
- [TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets](https://mlsanity.com/paper/2204.07615/) (2022)
- [Feature Interaction Aware Automated Data Representation Transformation](https://mlsanity.com/paper/2309.17011/) (2023)
- [Amortized Auto-Tuning: Cost-Efficient Bayesian Transfer Optimization for Hyperparameter Recommendation](https://mlsanity.com/paper/2106.09179/) (2021)
- [Privileged Zero-Shot AutoML](https://mlsanity.com/paper/2106.13743/) (2021)
- [Privacy-preserving Online AutoML for Domain-Specific Face Detection](https://mlsanity.com/paper/2203.08399/) (2022)
- [Self-Optimizing Feature Transformation](https://mlsanity.com/paper/2209.08044/) (2022)
- [Review of the state of the art in autonomous artificial intelligence](https://mlsanity.com/paper/2210.10659/) (2022)
- [How Low Can We Go: Trading Memory for Error in Low-Precision Training](https://mlsanity.com/paper/2106.09686/) (2021)
- [Deep Pipeline Embeddings for AutoML](https://mlsanity.com/paper/2305.14009/) (2023)
- [pTNAS: Progressive Neural Architecture Search for Tabular Data](https://mlsanity.com/paper/2403.10318/) (2024)
- [Naive Automated Machine Learning -- A Late Baseline for AutoML](https://mlsanity.com/paper/2103.10496/) (2021)
- [A General Recipe for Automated Machine Learning in Practice](https://mlsanity.com/paper/2308.15647/) (2023)
- [Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines](https://mlsanity.com/paper/2312.14770/) (2023)
- [Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)](https://mlsanity.com/paper/2202.10979/) (2022)
- [A Survey on Semantics in Automated Data Science](https://mlsanity.com/paper/2205.08018/) (2022)
- [A Survey of Open Source Automation Tools for Data Science Predictions](https://mlsanity.com/paper/2208.11792/) (2022)
- [Serenity: Library Based Python Code Analysis for Code Completion and Automated Machine Learning](https://mlsanity.com/paper/2301.05108/) (2023)
- [Clustering Indices based Automatic Classification Model Selection](https://mlsanity.com/paper/2305.13926/) (2023)
- [Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection](https://mlsanity.com/paper/2405.11349/) (2024)
- [Efficient Deep Learning Board: Training Feedback Is Not All You Need](https://mlsanity.com/paper/2410.14743/) (2024)

## Abstract (arXiv)

In order to achieve state-of-the-art performance, modern machine learning techniques require careful data pre-processing and hyperparameter tuning. Moreover, given the ever increasing number of machine learning models being developed, model selection is becoming increasingly important. Automating the selection and tuning of machine learning pipelines consisting of data pre-processing methods and machine learning models, has long been one of the goals of the machine learning community. In this paper, we tackle this meta-learning task by combining ideas from collaborative filtering and Bayesian optimization. Using probabilistic matrix factorization techniques and acquisition functions from Bayesian optimization, we exploit experiments performed in hundreds of different datasets to guide the exploration of the space of possible pipelines. In our experiments, we show that our approach quickly identifies high-performing pipelines across a wide range of datasets, significantly outperforming the current state-of-the-art.