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Blei Lab

@bleilab.bsky.social

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Machine learning lab at Columbia University. Probabilistic modeling and approximate inference, embeddings, Bayesian deep learning, and recommendation systems. 🔗 https://www.cs.columbia.edu/~blei/ 🔗 https://github.com/blei-lab

www.cs.columbia.edu

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Andrew Jesson @anndvision.bsky.social · Apr 23

i'm at ICLR this week

presenting at the morning poster session on thursday

excited to catch up with friends and collaborators, old and new

let's chat

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Blei Lab @bleilab.bsky.social · Dec 10

Check out our new paper about hypothesis testing the circuit hypothesis in LLMs! This work previously won a top paper award at the ICML mechanistic interpretability workshop, and we’re excited to share it at #Neurips2024

claudia shi @claudiashi.bsky.social · Dec 10

The circuit hypothesis proposes that LLM capabilities emerge from small subnetworks within the model. But how can we actually test this? 🤔

joint work with @velezbeltran.bsky.social @maggiemakar.bsky.social @anndvision.bsky.social @bleilab.bsky.social Adria @far.ai Achille and Caro

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Blei Lab @bleilab.bsky.social · Dec 2

Check out our new paper from the Blei Lab on probabilistic predictions with conditional diffusions and gradient boosted trees! #Neurips2024

Nicolas Beltran-Velez @velezbeltran.bsky.social · Dec 2

I am very excited to share our new Neurips 2024 paper + package, Treeffuser! 🌳 We combine gradient-boosted trees with diffusion models for fast, flexible probabilistic predictions and well-calibrated uncertainty.

paper: arxiv.org/abs/2406.07658
repo: github.com/blei-lab/tre...

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Samples y | x from Treeffuser vs. true densities, for multiple values of x under three different scenarios. Treeffuser captures arbitrarily complex conditional distributions that vary with x.

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