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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
If you’re designing incentives, regulations, or evaluation systems, this post offers a new framework for thinking about accountability. Read the full piece here: medium.com/beyond-incen... 🧵7/7
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 · 12/01/2026
The core lesson: When quality can’t be fully specified in advance, letting context and consequences flow over time outperforms even the best-designed metrics. Stop measuring the moment; start weighing the relationship. 🧵6/7
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Jean Czerlinski Ortega @jeanimal.bsky.social · 12/01/2026
Imagine: 🔹 Software vendors as long-term service providers with a reputational incentive to patch flaws instantly. 🔹 Academic authors who value sustained ownership of their claims—begging for replications as much as restaurants beg for 5-star reviews. 🧵5/7
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Jean Czerlinski Ortega @jeanimal.bsky.social · 12/01/2026
Think about hotel reviews. If a hotel “100 meters from the beach” is actually separated from the beach by a wall, past customers can warn future customers. What happens if we apply that same logic to software security or academic publishing? 🧵4/7
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Jean Czerlinski Ortega @jeanimal.bsky.social · 12/01/2026
In my blog post, I explore a different approach: Reputation Systems. Reputation transforms one-off, easily gamed transactions into ongoing relationships with accountability. It creates a bridge between today’s behavior and tomorrow’s consequences. 🧵3/7
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Jean Czerlinski Ortega @jeanimal.bsky.social · 12/01/2026
When quality is elusive, our instinct is to add more metrics. But Goodhart’s Law always wins: “When a measure becomes a target, it ceases to be a good measure.” Metrics offer temporary relief, but they eventually invite new, creative ways to fail (or be gamed). 🧵2/7
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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
7/ If you're in a domain where the cheaters move faster than the rulebook, "Hindsight Accountability" can help. Read more: 👉https://medium.com/@jeanimal/hindsight-accountability-deterring-the-gaming-of-regulations-2ccdc800db09 #Cybersecurity #AIRegulation #Incentives #Governance #PolicyDesign
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Jean Czerlinski Ortega @jeanimal.bsky.social · 21/05/2025
6/ This isn’t just a technical fix. It’s a philosophical shift in regulation: We don’t need to anticipate every trick—we just need to track evidence well enough to figure out the tricks later. Gamers will be deterred.
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Jean Czerlinski Ortega @jeanimal.bsky.social · 21/05/2025
5/ 🔐 Cybersecurity already uses these ideas. Firms track malware reports, identify new patterns over time, and retroactively patch their defenses. Some regulations now require these tracking systems. It’s hindsight, made actionable.
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Jean Czerlinski Ortega @jeanimal.bsky.social · 21/05/2025
4/ 🏦 In banking, clawbacks let firms reclaim bonuses for deals that later go bad. Even if the deal-makers snuck in a bad deal at the time, long-term performance still matters. That changes how people play the game.
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Jean Czerlinski Ortega @jeanimal.bsky.social · 21/05/2025
3/ 🏅 Sports agencies now freeze athletes’ biological samples for 10 years. When new drug tests emerge, they re-analyze. And sometimes strip medals retroactively. It’s not just punishment—it’s deterrence. Cheaters know the past can catch up.
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Jean Czerlinski Ortega @jeanimal.bsky.social · 21/05/2025
2/ Regulators leverage hindsight accountability when they: 1. Store evidence 2. Reanalyze the evidence with better tools & context later-- to catch people gaming the rules 3. Apply retroactive consequences It’s no silver bullet. But it can deter people from gaming in the first place.
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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
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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
Solving N equations in N unknowns is analogous to the interpolation threshold. Since there is exactly one solution, it has to fit any noise in the data. These are the shackles. Having fewer or more unknown parameters gives freedom to avoid overfitting. 4/4
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Jean Czerlinski Ortega @jeanimal.bsky.social · 20/10/2024
The spike in error happens at the interpolation threshold when the number of parameters in the model (same as number of columns for my regression) equals the number of examples (rows). Double descent follows. 3/4
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Jean Czerlinski Ortega @jeanimal.bsky.social · 20/10/2024
I create double descent with a few lines of sklearn code. I fit linear regression on data sampled with different “parameterization ratios,” (# examples / # parameters), allowing me to control exactly where the interpolation threshold causes the error spike before descent. 2/4
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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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