Mindful Modeler
Christoph MolnarTabular foundation models, ML interpretability, and beyond by a statistician turned machine learner.
| Platform |  | Pricing | Only free issues | Publishes | Weekly |
|---|---|---|---|---|---|
| Issues | 142 | Founded | 4 years ago | Last Issue | 7 days ago |
| Active | |||||
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mindfulmodeler.substack.comLatest Issues
How to make Tabular Foundation Model inference faster
The greatest bottleneck with tabular foundation models: Prediction, aka inference, is slow.
This post is a collection of tips and tricks to make tabular foundation models much faster. BUT! There is always a price to pay. And you must decid...
Time series forecasting with tabular foundation models
This post is a quick primer on using tabular foundation models for time series forecasting.
In my post titled I’m betting on tabular foundation models, I argued that TFMs will work with all kinds of supervised ML tasks because you can pre-...
Train or Test for Feature Effect Estimation? We Finally Have an Answer
This is a guest post by the Explainable AI researcher Timo Heiß (LMU Munich, Germany).
We use XAI methods to trust our models - but can we trust XAI itself? Ironically, XAI comes with its own trust problem: these methods are approximations...
Context is the new training
Tabular foundation models such as TabPFN and TabICL don’t need to be trained to perform regression or classification. What they do is called in-context learning. What used to be the training data now becomes the context data at prediction t...
Regression should predict full distributions
When I took on a side quest to forecast water supply, I had to predict the 10%, 50%, and 90% quantiles. I solved this by training three separate quantile models (ensembles of xgboost, actually). I would have preferred to only train a single...
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