# OBOE: Collaborative Filtering for AutoML Model Selection

arXiv: https://arxiv.org/abs/1808.03233 · HTML: https://mlsanity.com/paper/1808.03233/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2018.

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

- [OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.08860/) (2023)
- [LightAutoML: AutoML Solution for a Large Financial Services Ecosystem](https://mlsanity.com/paper/2109.01528/) (2021)
- [Towards Green Automated Machine Learning: Status Quo and Future Directions](https://mlsanity.com/paper/2111.05850/) (2021)
- [Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey](https://mlsanity.com/paper/2411.10478/) (2024)
- [A Unified Sampling Framework for Solver Searching of Diffusion Probabilistic Models](https://mlsanity.com/paper/2312.07243/) (2023)
- [Transfer Learning based Search Space Design for Hyperparameter Tuning](https://mlsanity.com/paper/2206.02511/) (2022)
- [Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2208.12433/) (2022)
- [Collaborative Performance Prediction for Large Language Models](https://mlsanity.com/paper/2407.01300/) (2024)
- [Naive Automated Machine Learning](https://mlsanity.com/paper/2111.14514/) (2021)
- [AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks](https://mlsanity.com/paper/2303.07669/) (2023)
- [Agentic AI framework for End-to-End Medical Data Inference](https://mlsanity.com/paper/2507.18115/) (2025)
- [A Scalable AutoML Approach Based on Graph Neural Networks](https://mlsanity.com/paper/2111.00083/) (2021)
- [Task Selection for AutoML System Evaluation](https://mlsanity.com/paper/2208.12754/) (2022)
- [TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets](https://mlsanity.com/paper/2204.07615/) (2022)
- [AutoXPCR: Automated Multi-Objective Model Selection for Time Series Forecasting](https://mlsanity.com/paper/2312.13038/) (2023)
- [Overtuning in Hyperparameter Optimization](https://mlsanity.com/paper/2506.19540/) (2025)
- [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)
- [SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions](https://mlsanity.com/paper/2202.10451/) (2022)
- [The Potential of AutoML for Recommender Systems](https://mlsanity.com/paper/2402.04453/) (2024)
- [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)
- [Automatic model training under restrictive time constraints](https://mlsanity.com/paper/2104.10746/) (2021)
- [An experimental survey and Perspective View on Meta-Learning for Automated Algorithms Selection and Parametrization](https://mlsanity.com/paper/2504.06207/) (2025)
- [(Machine) Learning to Improve the Empirical Performance of Discrete Algorithms](https://mlsanity.com/paper/2109.14271/) (2021)
- [A Survey of Open Source Automation Tools for Data Science Predictions](https://mlsanity.com/paper/2208.11792/) (2022)
- [A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence](https://mlsanity.com/paper/2407.12445/) (2024)

## Abstract (arXiv)

Algorithm selection and hyperparameter tuning remain two of the most challenging tasks in machine learning. Automated machine learning (AutoML) seeks to automate these tasks to enable widespread use of machine learning by non-experts. This paper introduces OBOE, a collaborative filtering method for time-constrained model selection and hyperparameter tuning. OBOE forms a matrix of the cross-validated errors of a large number of supervised learning models (algorithms together with hyperparameters) on a large number of datasets, and fits a low rank model to learn the low-dimensional feature vectors for the models and datasets that best predict the cross-validated errors. To find promising models for a new dataset, OBOE runs a set of fast but informative algorithms on the new dataset and uses their cross-validated errors to infer the feature vector for the new dataset. OBOE can find good models under constraints on the number of models fit or the total time budget. To this end, this paper develops a new heuristic for active learning in time-constrained matrix completion based on optimal experiment design. Our experiments demonstrate that OBOE delivers state-of-the-art performance faster than competing approaches on a test bed of supervised learning problems. Moreover, the success of the bilinear model used by OBOE suggests that AutoML may be simpler than was previously understood.