Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments
Combines fast experiments with Bayesian optimization to enhance long-term outcomes, reducing experiment time by 60%.
Key Findings
Methodology
The paper proposes a parallel experimental design combining fast and long-term experiments, leveraging Bayesian optimization to optimize large action spaces in a short time. Multi-task Gaussian Processes and target-aware Gaussian Processes are used to convert short-term proxy information into long-term outcome predictions.
Key Results
- Result 1: In real-world experiments at Meta, experiment time was reduced by 60% while achieving superior optimization results.
- Result 2: Multi-task Gaussian Process models effectively identified relevant short-term proxies, improving long-term outcome prediction accuracy.
- Result 3: The proposed method performed excellently in optimizing long-term outcomes in dynamic A/B testing environments.
Significance
This research is significant for both academia and industry, addressing pain points in long-term effect optimization. By combining fast experiments with Bayesian optimization, it significantly reduces experiment time and improves optimization efficiency, offering new insights for tuning internet systems.
Technical Contribution
Technical contributions include a new experimental design framework combining short-term and long-term experiments, using Bayesian optimization for rapid optimization of large action spaces. The introduction of multi-task Gaussian Processes and target-aware Gaussian Processes enhances the utilization of short-term proxy information.
Novelty
This study is the first to combine fast experiments with Bayesian optimization, proposing a novel experimental design framework that significantly improves optimization efficiency and prediction accuracy compared to existing methods.
Limitations
- Limitation 1: The method relies on the accuracy of short-term proxies, and proxy errors may lead to optimization bias.
- Limitation 2: In some cases, significant computational resources may be required to handle large-scale data.
Future Work
Future work could explore more types of short-term proxies to further improve long-term outcome prediction accuracy and optimize the use of computational resources.
AI Executive Summary
Online experiments in internet systems, such as A/B tests, are widely used for system tuning problems. However, optimizing for long-term effects often requires lengthy experiments, making traditional sequential experimentation strategies impractical.
This paper proposes a novel approach combining fast experiments and offline proxies with long-running, slow experiments to perform Bayesian optimization over large action spaces in a short time. Multi-task Gaussian Processes and target-aware Gaussian Processes convert short-term proxy information into predictions of long-term outcomes.
In real-world experiments at Meta, this method significantly reduced experiment time while achieving superior optimization results. This research provides new insights for tuning internet systems and holds significant academic and industrial implications.
Deep Analysis
Background
Online experiments are crucial in the product development cycle at internet firms, used to evaluate product changes and drive improvements. A/B testing is frequently used for complex tuning problems, such as optimizing recommender system ranking policies. As technology evolves, Bayesian optimization has become an efficient tuning method.
Core Problem
The core problem is optimizing long-term outcomes in a short time. Traditional sequential experimentation strategies often require lengthy experiments, which are impractical in practice. Thus, a new method is needed to quickly and effectively optimize long-term outcomes.
Innovation
The core innovation lies in proposing a parallel experimental design combining fast experiments with Bayesian optimization. Multi-task Gaussian Processes and target-aware Gaussian Processes convert short-term proxy information into long-term outcome predictions, enhancing optimization efficiency.
Methodology
- �� Use parallel experimental design combining fast and long-term experiments.
- �� Leverage Bayesian optimization to optimize large action spaces quickly.
- �� Employ multi-task Gaussian Processes and target-aware Gaussian Processes to enhance the utilization of short-term proxy information.
Experiments
The experimental design includes multiple real-world experiments at Meta, using multi-task Gaussian Process models and target-aware Gaussian Process models to convert short-term proxy information into long-term outcome predictions. Results show that this method significantly reduced experiment time while achieving superior optimization results.
Results
Results show that in real-world experiments at Meta, experiment time was reduced by 60% while achieving superior optimization results. Multi-task Gaussian Process models effectively identified relevant short-term proxies, improving long-term outcome prediction accuracy.
Applications
This method can be directly applied to tuning internet systems, such as optimizing recommender system ranking policies. By reducing experiment time and improving optimization efficiency, it provides new solutions for the industry.
Limitations & Outlook
The method relies on the accuracy of short-term proxies, and proxy errors may lead to optimization bias. In some cases, significant computational resources may be required to handle large-scale data. Future work could explore more types of short-term proxies to further improve long-term outcome prediction accuracy.
Plain Language Accessible to non-experts
Imagine a factory where workers need to find the best way to improve product quality. Traditional methods require long experiments to observe effects, like workers needing weeks to test different production lines. This paper's method is like introducing rapid testing equipment in the factory, allowing quick testing of various production methods and predicting long-term effects. This approach combines fast testing with long-term observation, helping the factory find the best production method faster.
ELI14 Explained like you're 14
Imagine playing a game where you need to find the best strategy to win. Traditional methods are like playing for a long time each time to see the effects. This paper's method is like having a fast simulator that can test many strategies quickly and predict long-term effects. This way, you can find the best strategy faster and win the game!
Glossary
Bayesian Optimization
A method based on Bayesian statistics for decision-making under uncertainty.
Used to optimize long-term outcomes in large action spaces.
A/B Testing
An online experiment method comparing two or more variants to evaluate effects.
Used to evaluate ranking policies in recommender systems.
Multi-task Gaussian Process
A probabilistic model for handling multi-task learning problems.
Used to convert short-term proxy information into long-term outcome predictions.
Long-term Outcomes
The long-term impact of system changes on user behavior.
The optimization goal is to improve long-term outcomes.
Short-term Proxies
Short-term metrics used to predict long-term outcomes.
Enhances long-term outcome prediction accuracy using short-term proxy information.
Open Questions Unanswered questions from this research
- 1 How to further improve the accuracy of short-term proxies to reduce optimization bias.
- 2 How to optimize computational resource usage in large-scale data environments.
Applications
Immediate Applications
Recommender System Optimization
Enhance ranking policies in recommender systems by reducing experiment time and improving optimization efficiency.
Long-term Vision
Large-scale System Tuning
Provide a rapid and effective system tuning method for internet companies, improving product quality and user experience.
Abstract
Online experiments in internet systems, also known as A/B tests, are used for a wide range of system tuning problems, such as optimizing recommender system ranking policies and learning adaptive streaming controllers. Decision-makers generally wish to optimize for long-term treatment effects of the system changes, which often requires running experiments for a long time as short-term measurements can be misleading due to non-stationarity in treatment effects over time. The sequential experimentation strategies--which typically involve several iterations--can be prohibitively long in such cases. We describe a novel approach that combines fast experiments (e.g., biased experiments run only for a few hours or days) and/or offline proxies (e.g., off-policy evaluation) with long-running, slow experiments to perform sequential, Bayesian optimization over large action spaces in a short amount of time.