noahho

6 ms·

1.
by noahho 25d ago
Prior Labs | Berlin, Freiburg, NYC | ONSITE | Full-time | ML Infra, Research Scientist/Engineer, Backend, Full Stack We build foundation models for tabular data. Deep learning transformed text and images but mostly skipped tables, whic
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by noahho 2mo ago
Prior Labs | Berlin / Freiburg / NYC | ONSITE | Full-time | Multiple Roles | https://priorlabs.ai Deep learning transformed text and images but mostly skipped tables - the data behind most clinical trials, financial mo
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by noahho 3mo ago
Prior Labs | Berlin / Freiburg / NYC | ONSITE | Full-time | Multiple Roles | https://priorlabs.ai/ Deep learning transformed text and images but mostly skipped tables, even though they're behind most clinical
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by noahho 3mo ago
They don't show the strongest TabPFN variants in the plot unfortunately, i.e. thinking and ensembled. Not really apples to apples.
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by noahho 5mo ago
Prior Labs | Berlin / Freiburg / NYC | ONSITE | Full-time | Multiple Roles Tables power every financial model, clinical trial, and scientific experiment, but deep learning has mostly ignored them. No natural sequence, no spatial s
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by noahho 6mo ago
Prior Labs | Berlin / Freiburg / NYC | ONSITE | Full-time | Multiple Roles Tables power every clinical trial, financial model, and scientific experiment, but deep learning has mostly ignored them. No natural sequence, no spatial s
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by noahho 7mo ago
Prior Labs | Berlin / Freiburg / NYC | ONSITE & REMOTE (EU) | Full-time Tables power every clinical trial, financial model, and scientific experiment, but deep learning has mostly ignored them. No natural sequence, no spatial
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TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models(arxiv.org)
7 points by noahho 10mo ago|0 comments
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by noahho 11mo ago
Yes exactly, the API is the best way to handle text features. The actual semantics often matter a lot . Is the API an option for you or would you need this local?
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by noahho 11mo ago
Less feature engineering is definitely something we are aiming for. The current version is actually only based on statistics, the real world connections between features is something we're working on right now and hope to show results
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by noahho 11mo ago
When we released TabPFNv1 over three years ago, I didn’t expect at all the hundreds of comments and reposts we would see. Tabular data had been a field getting little love from AI research—but we immediately felt that this was a topic that
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by noahho 11mo ago
TabPFN-2.5 default (one forward pass) matches AutoGluon 1.4 tuned for four-hours. Autogluon is the strongest AutoML including stacking of XGB and cat boost and even includes the previous TabPFNv2.
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by noahho 1y ago
Prior Labs | Founding-Team: Software Engineer, Data Scientist, ML Engineer, Product Manager, Developer Relations| On-Site (Berlin, Freiburg) | Full-time Prior Labs is building foundation models for structured/tabular data – AI's b
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AI's Blind Spot: The Structured Data Challenge(priorlabs.ai)
2 points by noahho 1y ago|0 comments
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by noahho 1y ago
Prior Labs | Founding-Team: Software Engineer, Data Scientist, ML Engineer, Product Manager, Developer Relations| On-Site (Berlin, Freiburg) | Full-time Prior Labs is building foundation models for structured/tabular data – AI's b
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by noahho 2y ago
Prior Labs | Software Engineer, Product Manager, Developer Relations, ML Engineer | On-Site (Berlin, Freiburg) | Full-time AI has transformed text, images, and code—but structured data remains overlooked. Prior Labs is an early-stage startu
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by noahho 2y ago
Thanks a lot! Currently have an issue on documenting how to use for more samples at https://github.com/PriorLabs/TabPFN/issues/129 . Will do this soon, maybe give an upvote there if it matters to you.
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by noahho 2y ago
Yes! This makes sense from a learning perspective: More samples add additional evidence the datapoint is actually what you observed - based on one sample the model is closer to a mean regression (which would translate to more balanced class
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by noahho 2y ago
if you're predicting on text data, our public models don't do that, they would encode as classes. Our API ( https://github.com/PriorLabs/tabpfn-client/ ) has experimental support.
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by noahho 2y ago
Thanks a lot! We don't see clear artifacts for the synth data. Part of the "trick" is to keep the capacity of our model low, it has only about 11M parameters. That forces the model to "learn an in-context learning algori
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by noahho 2y ago
thanks a ton! If it's public please share in the Discord https://discord.com/channels/1285598202732482621/ > #use-cases (just created!), if not, mail me at noah@priorlabs.ai
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by noahho 2y ago
Looks like a great use case! We have a method specifically for imputation in the tabpfn-extensions package ( https://github.com/PriorLabs/tabpfn-extensions/blob/dbc3f5da... ). It needs some cleaning up before I
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by noahho 2y ago
Up to 4 hrs of tuning per dataset / split (10-fold CV)
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by noahho 2y ago
Author here! The fundamental challenge is that LLMs like O1 and Claude 3.5 simply aren't built for the unique structures of tabular data. When processing tables through LLMs, the inefficiencies quickly become apparent - tokenizing a 10
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by noahho 2y ago
Author here! The breast cancer dataset is simple and heavily saturated, so small differences between methods are expected. As you say, single-use examples can be noisy due to randomness in how the data is randomly split into training and te
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Automating Feature Engineering with LLMs(medium.com)
2 points by noahho 3y ago|0 comments
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Show HN: Automating Feature Engineering with LLMs(github.com)
1 points by noahho 3y ago|0 comments
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by noahho 3y ago
LLMs meet AutoML: in an effort to integrate user knowledge into AutoML, our new tool CAAFE uses LLMs to generate semantically meaningful features for tabular data (and also explains them). Towards an AI assistant for human data scientists P
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Automating DataScience Using LLMs(pypi.org)
4 points by noahho 3y ago|1 comments