
Tabular foundation models, ML interpretability, and beyond by a statistician turned machine learner.
| Platform | Pricing | Only free issues | Publishes | Weekly | |
|---|---|---|---|---|---|
| Issues | 151 | Founded | 4 years ago | Last Issue | 5 days ago |
| Active | |||||

Sometimes you have to make a big change in life. You’ve probably been there yourself.
For me, this time is now.
In 2022, I made the scary decision to become self-employed as a writer for technical books. With the Interpretable ML book alr...
Google Research published TabFM, a new tabular foundation model.
The AI influencers found out, and social media is now awash in TabFM posts.
Let’s talk about what TabFM is and isn’t, without the hype.
It’s not a secret that most ML models for quantile regression tend to be too biased towards the mean. You want a 10% quantile? The model’s quantile estimate will tend to be too large. And for large quantiles, the estimate is often too low....
As you might have noticed, I’m rather optimistic about tabular foundation models. And while TFMs have been gaining momentum, beating benchmark after benchmark, they don’t outperform every time. And it’s not just random tasks here and there,...
When I read the TabPFN-3 Technical Report, the benchmark for “TabPFN-3-Thinking” stood out: it appeared at the top of the TabArena benchmark for large datasets. TabPFN-3-Thinking also surpassed AutoGluon 1.5 extreme, an open-source AutoML f...
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The writers behind this newsletter.
Writer and machine learning expert
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