Machine learning lab at Columbia University. Probabilistic modeling and approximate inference, embeddings, Bayesian deep learning, and recommendation systems.
New York, NY
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We have a new paper on topic models!
We introduce Mechanistic Topic Models (MTMs). MTMs model SAE feature counts rather than words or plain embeddings, combining the benefits of LLM-based and probabilistic topic models.
Paper (published in TACL) : arxiv.org/abs/2507.23220
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We show that MTMs are better than traditional and neural baselines at finding salient and abstract themes, while costing less than LLM-based methods.
Moreover, working with SAE features allows you to construct topic steering vectors to steer LLM generation toward MTM topics.
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We are presenting a poster at EMNLP in Budapest next month!
Github: github.com/blei-lab/mechanis…
With @carolinazheng_, @nicolasvelezb, @SwetaKarlekar, @causalclaudia, @AchilleNazaret, Asif Mallik, @amir_feder, and David Blei.
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Blei Lab retweeted
1/ Two new preprints on OOD generalization.
Shared lesson: the goal should not always be to find what is invariant.
For good prediction in new environments, it can be better to model environment variation explicitly, then marginalize it out.
arXiv:2604.26128
arXiv:2606.05365
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Blei Lab retweeted
Announcing our next talk with NYU Professor @_romain_lopez_: "Learning from Millions of Cells with Deep Generative Models"
Registration: eventbrite.com/e/ml-nyc-spea…
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Blei Lab retweeted
I am on the academic job market this year! My research advances probabilistic machine learning and its application to biology. I'm looking for faculty positions in stats/CS/applied math and in bio departments.
Academic website: eweinstein.github.io/
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Check out our new paper using hierarchical causal models and representation learning to estimate the effects of immune proteins
I'm thrilled to present a new paper with @blei_lab and @lizbwood about estimating the causal effects of T cell receptors in humans. arxiv.org/abs/2410.14127
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New paper from @blei_lab member @EliWeinstein6 on manufacturing samples from generative protein models. The idea is to approximate a complex distribution with simpler ones that are easy to sample from in the real world, using stochastic chemical reactions.
I’m thrilled to announce: at @jura_bio we’ve constructed a new kind of generative protein model, which allows us to manufacture its generated designs in the real world at petascale.
Paper: static1.squarespace.com/stat…
Blog: jura.bio/blog/variationalsyn…
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Blei Lab retweeted
Excited to announce that ML-NYC is back this semester. Our first speaker is Bin Yu from UC Berkeley on Monday Sept 23rd 4pm. Register here: eventbrite.com/e/ml-nyc-spea…
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Blei Lab retweeted
Excited to have Michael Kearns to be our next ML-NYC speaker on April 10th! eventbrite.com/e/ml-nyc-spea…
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Blei Lab retweeted
Excited to host Shirley Ho as our next ML-NYC speaker!
eventbrite.com/e/ml-nyc-spea…
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Blei Lab retweeted
I’m very excited about my new paper with @blei_lab : Hierarchical Causal Models
Preprint: arxiv.org/abs/2401.05330
Talk: piped.video/watch?v=ovOVaNMx…
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Blei Lab retweeted
Excited to host Tamara Broderick as our next ML-NYC speaker! eventbrite.com/e/ml-nyc-spea…
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mike jordan walking around trieste and talking about AI
piped.video/3lXphIYfoBM
a version in italian!
piped.video/-caiEVP4Sjo
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Blei Lab retweeted
Excited to welcome Tom Griffiths @cocosci_lab as a our next ML-NYC speaker!
eventbrite.com/e/ml-nyc-spea…
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Blei Lab retweeted
We're excited to welcome Richard Zemel as our next speaker on Wednesday Oct 18th at 4pm!
eventbrite.com/e/ml-nyc-spea…
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Dave is giving two talks at Berkeley next week!
events.berkeley.edu/BIDS/eve…
climb.berkeley.edu/hierarchi…
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Blei Lab retweeted
We're excited to welcome Daniel Lee as our next speaker on Wednesday September 20th at 4pm!
The event will be followed by a happy hour hosted by the Flatiron Institute
Register here: eventbrite.com/e/ml-nyc-spea…
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Blei Lab retweeted
Belated updates:
1- I defended my PhD in May! Big thank you to my committee and especially to my advisor, @blei_lab.
2- I'm excited to start a postdoc at Harvard! Very grateful to @harvard_data for the opportunity.
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