Now might be the worst possible point in time to admit that I don’t own a physical copy of the book myself (yet!! I’m actually building up a textbook bookshelf for myself) BUT because Hastie, Tibshirani & Friedman are the GOATs that they are, they made the pdf free: hastie.su.domains/ElemStatLearn/

Oh friends who are complaining about not enough Real Math^tm in their feed, I am here to help. Well, Alicia is here to help, at least!

To emphasise just how accurately that reflects Alan’s approach to research (which I 100% subscribe to btw), I feel compelled to share that this is the actual slide I use whenever I present the U-turn paper in Alan’s absence 😂 (not a joke)

btw this is why friends dont let friends skip the “boring classical ML” chapters in Elements of Statistical Learning‼️ (True story: the origin of this case study is that @alanjeffares.bsky.social[big EoSL nerd] looked at the neural net eq&said “kinda looks like GBTs in EoSL Ch10”&we went from there)

Part 2: Why do boosted trees outperform deep learning on tabular data?? @alanjeffares.bsky.social & I suspected that answers to this are obfuscated by the 2 being considered very different algs🤔 Instead we show they are more similar than you’d think — making their diffs smaller but predictive!🧵1/n

Don’t need to know much about causal inference to know that in the counterfactual world where I didn’t join this platform I would have missed out on absolute GOLD content like this thread 🤩 ITE(join Bluesky)>>>0

If it’s selection, we will find you. If it’s causal, we will make you.

aren’t smoothers just THE BEST?? understanding double descent, random forests, neural network complexity, and now causal inference — smoothers are just at your service when you need them

New WP 🚨 1. Recipe to write estimators as weighted outcomes 2. Double ML and causal forests as weighting estimators 3. Plug&play classic covariate balancing checks 4. Explains why Causal ML fails to find an effect of 1 with noiseless outcome Y = 1 + D 5. More fun facts arxiv.org/abs/2411.11559

From double descent to grokking, deep learning sometimes works in unpredictable ways.. or does it? For NeurIPS(my final PhD paper!), @alanjeffares.bsky.social & I explored if&how smart linearisation can help us better understand&predict numerous odd deep learning phenomena — and learned a lot..🧵1/n

I finally decided to double up with an account here hoping to find more scientific discourse :) So: Hi, I’m Alicia, Machine Learning Researcher at MSR (since last month)! Prev I was a PhD student in Cambridge trying to make sense of the mysteries of modern Machine Learning (— to be continued!!) :)