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Jean Czerlinski Ortega

@jeanimal.bsky.social
375 followers 248 following 20 posts

Sometimes Google engineer modeling things and celebrating non-things: machine learning, incentives, behavior, ethics, physics. Former member of Gigerenzer's Adaptive Behavior and Cognition group.

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Jean Czerlinski Ortega @jeanimal.bsky.social · 12/01/2026
Do you work in a domain where low quality is revealed only after a decision is made? A hotel looks perfect online, software ships as “secure,” or a paper passes peer review—but the real issues only surface months later. Here is why metrics fail us and why reputation is the answer. 🧵1/7
medium.com
How Reputation Captures What Metrics Cannot
Fixing gaming in one-off transactions with reputation systems
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Jean Czerlinski Ortega @jeanimal.bsky.social · 21/05/2025
🧵1/ Fast-moving domains like cybersecurity evolve too quickly for static rules. Adaptive regulation has scheduled review and updates, but hackers evolve faster. An approach I call “hindsight accountability” can help: medium.com/@jeanimal/hi...
medium.com
Hindsight Accountability: Deterring the Gaming of Regulations
From sports dopers to hackers, some cheaters can only be caught in hindsight
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Jean Czerlinski Ortega @jeanimal.bsky.social · 02/03/2025
LLM-lasso keeps the theory of Lasso, while using an LLM to analyze domain-specific metadata to improve the weights of the regularizer. Result: better performance on biomedical case studies. Plus, since lasso reduces the number of features, it's more interpretable! arxiv.org/abs/2502.10648
arxiv.org
LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization
We introduce LLM-Lasso, a novel framework that leverages large language models (LLMs) to guide feature selection in Lasso $\ell_1$ regression. Unlike traditional methods that rely solely on numerical ...
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Reposted by Jean Czerlinski Ortega
Andreas Kirsch @blackhc.bsky.social · 17/12/2024
The slides for my lectures on (Bayesian) Active Learning, Information Theory, and Uncertainty are online now 🥳 They cover quite a bit from basic information theory to some recent papers: blackhc.github.io/balitu/ and I'll try to add proper course notes over time 🤗
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Reposted by Jean Czerlinski Ortega
Clem Delangue 🤗 @clem.hf.co · 16/12/2024
Just 10 days after o1's public debut, we’re thrilled to unveil the open-source version of the technique behind its success: scaling test-time compute By giving models more "time to think," Llama 1B outperforms Llama 8B in math—beating a model 8x its size. The full recipe is open-source!
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Jean Czerlinski Ortega @jeanimal.bsky.social · 20/10/2024
Double descent enables a chat bot with a billion parameters to perform well and not overfit. But how does double descent work? I use simulations fitting linear regressions, plots, and tables for solving systems of equations to build intuition. medium.com/@jeanimal/ho...
medium.com
How double descent breaks the shackles of the interpolation threshold
Insights for deep learning from solving N equations with N unknowns
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