Summary of A Sparse Pac-bayesian Approach For High-dimensional Quantile Prediction, by the Tien Mai
A sparse PAC-Bayesian approach for high-dimensional quantile prediction
by Tien Mai
First submitted to arxiv on: 3 Sep 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 This novel probabilistic machine learning approach for high-dimensional quantile prediction uses a pseudo-Bayesian framework with a scaled Student-t prior and Langevin Monte Carlo for efficient computation. The method, which leverages PAC-Bayes bounds to establish non-asymptotic oracle inequalities, demonstrates strong theoretical guarantees, including minimax-optimal prediction error and adaptability to unknown sparsity. Simulation results and real-world data validation show competitive performance against established frequentist and Bayesian techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to predict quantiles in situations where there are many variables (high-dimensional settings). The approach uses a combination of statistical methods called pseudo-Bayesian, scaled Student-t prior, and Langevin Monte Carlo. It shows that this method works well in practice and is competitive with other established techniques. |
Keywords
* Artificial intelligence * Machine learning
