Interested in how AI providers can alter their products to evade regulation, the possible defenses and what it means for AI governance?
Or just want a nice soothing voice to nap after lunch?
I'm defending on Feb. 10 at 2:30PM, Paris time.
3y of PhD distilled in 45min! 🔗in🧵
Can ML models be trained under fairness constraints with formal DP guarantees, without sacrificing utility?
New paper at ICLR 2026: RaCO-DP
Poster: Pavilion 4 (later today!)
🧵
(w/ @tudorcebere, Michael Menart, Aurélien Bellet, @NicolasPapernot )
Wassim's most important PhD contribution was to discover 𝐈𝐧𝐝𝐢𝐫𝐞𝐜𝐭 𝐃𝐚𝐭𝐚 𝐏𝐨𝐢𝐬𝐨𝐧𝐢𝐧𝐠 (poisoning with data that is absent from the training set).
As often in AI, the prior work of a PhD student gets invisibilised by announcements from large teams.
Let's fix that
New research: cheaply detecting changes in LLM APIs.
We published two papers on the topic:
- Log Probability Tracking of LLM APIs (ICLR 2026)
- Token-Efficient Change Detection in LLM APIs
Both papers request a single token of output from APIs, enabling unprecedently cheap monitoring.
PhDone! A few weeks ago, I defended my contributions to avoid a dieselgate moment for AI regulation.
After some rest, I am now entering a blissful period of pure academic freedom, courtesy of the French unemployment benefits 🇫🇷 Time to look for a new academic home !
The defense, and all the work that was distilled into it, would not have been so enjoyable without the guidance from my advisors Gilles, Erwan, Francois, Camilla and Gohar.
To tune in remotely, see https://t.co/fjR05gzP3n
I'm also on the job market, here are some interests:
trustworthy ML, AI governance, outsider system scrutiny, platforms attempts at evading regulations
Interested in how AI providers can alter their products to evade regulation, the possible defenses and what it means for AI governance?
Or just want a nice soothing voice to nap after lunch?
I'm defending on Feb. 10 at 2:30PM, Paris time.
3y of PhD distilled in 45min! 🔗in🧵
In the AI ecosystem, who supplies the data? the compute? the models?
We just released a new tool on the AI Supply Chain. Our dataset reveals how AI models, data, compute, capital, and even talent change hands.
Here’s why you should care 👇
📯 ICML25 spotlight 📯 How to detect and prevent audit manipulations?
Do you remember 🚗 Dieselgate 💨? The car computer would detect when it was on a test-bench and reduce the engine power to fake environmental compliance. Well, this can happen in AI too. 🧵1/6
@trustworthy_ml@icmlconf Current incentives for "Trustwortyness" are reputational. Thus, models developers only need to fake it. We present a research direction to prevent this:
https://t.co/XQzpSIoaNW
📯 ICML25 spotlight 📯 How to detect and prevent audit manipulations?
Do you remember 🚗 Dieselgate 💨? The car computer would detect when it was on a test-bench and reduce the engine power to fake environmental compliance. Well, this can happen in AI too. 🧵1/6
We're excited to announce the Call for Papers for SaTML 2026, the premier conference on secure and trustworthy machine learning @satml_conf
We seek papers on secure, private, and fair learning algorithms and systems.
👉 https://t.co/cPFitlsXu2
⏰ Deadline: Sept 24
📢 New ICML 2025 paper!
Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention
🤔 Think model uncertainty can be trusted?
We show that it can be misused—and how to stop it!
Meet Mirage (our attack💥) & Confidential Guardian (our defense🛡️).
🧵1/10
📯 ICML25 spotlight 📯 How to detect and prevent audit manipulations?
Do you remember 🚗 Dieselgate 💨? The car computer would detect when it was on a test-bench and reduce the engine power to fake environmental compliance. Well, this can happen in AI too. 🧵1/6
🧵6/6 If you want to read more about this, I encourage you to read the paper, but not only!
Recently, there has been a lot of exciting works on robust audits, here are a few I enjoyed:
- https://t.co/ST0bum51KG
- https://t.co/hD5oslLr0t
- https://t.co/ww5C6JVnff