Adam L reposted
New working paper with @corymccartan.com on the Gaussian Process limit of BART! (Bayesian Additive Regression Trees... not the Bay Area Rapid Transit sadly) arxiv.org/abs/2607.28844
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Adam L
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Data Scientist. Poli sci PhD. Cyclist (Gravel, MTB, road). Bayes, causal inference, etc. Also: cats, food opinions. Views & opinions my own. Not investment advice. He/him
What they're sharing
Seeing the Forest for the Trees: The Gaussian Process Limit of BART arxiv.org
View on Bluesky →
Bounding Treatment Effects by Pooling Limited Information across Observations arxiv.org
View on Bluesky →
Episode 193 - Is AI Coming for Our Jobs?! podcasts.apple.com
Causal Inference with Missing Exposures and Missing Outcomes arxiv.org
probml.github.io
From Frequency to Meaning: Vector Space Models of Semantics | Journal of Artificial Intelligence Research jair.org
“Probabilistic machine learning”: a book series by Kevin Murphy probml.github.io
Articles & links
Adam L reposted
"Bounding Treatment Effects by Pooling Limited Information across Observations" Link: arxiv.org/abs/2111.05243 Partial pooling applied to partial identification! Cool idea. R package: cran.r-project.org/web/packages... polisky 📉📈 #stats
A really interesting discussion of AL (LLMs) for athletic coaching, from experienced coaches. Their take: the training plans it gives you are good, but as w/many domains, you'll get the most out of it if you have some background knowledge. Overall they're positive, but with ca…
This is a very cool looking paper, but the title ("Causal Inference with Missing Exposures and Missing Outcomes") is funny to me: it sounds like you can estimate treatment effects when you're missing both exposures and outcomes for the same observation. Paper: arxiv.org/abs/25…
If you read Kevin Murphy’s 2012 book “Machine Learning: a Probabilistic Perspective,” an encyclopedic tome for ML, he devotes only one chapter to neural net methods (at the end). NNs were basically seen as curiosities! (Free via author: probml.github.io/pml-book/boo...)
similarly, if you read this 2010 article on pre-NN vectorization, vectors are basically curiosities - they're interesting, but not that effective: www.jair.org/index.php/ja... It's a great article, because it's *right* before NNs changed everything.
The newer versions, released in 2022 & 2023, have much more NN/DL stuff, because the combination of GPUs, Cuda, and Tensorflow was a game-changer! There was no longer a need to calculate gradients by hand, and optimize code manually.
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