Summary of The Computational Curse Of Big Data For Bayesian Additive Regression Trees: a Hitting Time Analysis, by Yan Shuo Tan et al.
The Computational Curse of Big Data for Bayesian Additive Regression Trees: A Hitting Time Analysis
by Yan Shuo Tan, Omer Ronen, Theo Saarinen, Bin Yu
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Bayesian Additive Regression Trees (BART) model is a popular tool for causal inference and beyond, thanks to its strong predictive performance backed by theoretical guarantees. Researchers have observed that the BART sampler can converge slowly, and this paper confirms those findings. The study shows that when covariates are discrete, the Markov chain’s hitting time increases with the training sample size (n), leading to differences between approximate and exact posteriors. Simulations demonstrate worsening frequentist undercoverage for approximate posterior intervals and a growing ratio between the MSE of the approximate posterior and an improved sampler. The paper discusses potential improvements to BART sampler convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BART is a special kind of machine learning model that’s great at making predictions. It’s used in many fields, including trying to figure out cause-and-effect relationships. Researchers have found that when they use this model to make predictions, it takes a while for the results to settle down. This paper looks at why this happens and what it means. They tested the model with different amounts of data and saw that as the amount of data grew, the results became less accurate. The researchers also ran simulations to show how this could affect real-world applications. |
Keywords
» Artificial intelligence » Inference » Machine learning » Mse » Regression
