Summary of A Simple Baseline For Predicting Events with Auto-regressive Tabular Transformers, by Alex Stein and Samuel Sharpe and Doron Bergman and Senthil Kumar and C. Bayan Bruss and John Dickerson and Tom Goldstein and Micah Goldblum
A Simple Baseline for Predicting Events with Auto-Regressive Tabular Transformers
by Alex Stein, Samuel Sharpe, Doron Bergman, Senthil Kumar, C. Bayan Bruss, John Dickerson, Tom Goldstein, Micah Goldblum
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Machine Learning (stat.ML)
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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 proposed method uses standard autoregressive LLM-style transformers with elementary positional embeddings and a causal language modeling objective to predict properties of new events from historic data. This approach outperforms existing techniques across various datasets, making it a flexible baseline for different use-cases such as predicting labels, imputing missing values, or modeling event sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using old events to guess what will happen in the future. For example, can we tell if a credit card transaction is fake or how good someone will think a product is? Right now, people use special tricks to make this work, but these methods are complicated and only work for specific situations. The researchers came up with a simple way to do this using language models that learn from patterns in data. This new method works better than the old ways on lots of different datasets and can be used for many different tasks. |
Keywords
» Artificial intelligence » Autoregressive
