A true francophone!

Génial! Est ce que ce sera enregistré?

Huge thanks to my co-authors 🙏
Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Jörn Jacobsen, Marco Cuturi.

We hope RoPE helps reframe model misspecification as a learning problem that requires real-world data to be solved.

This is just the beginning.

We hope RoPE pushes SBI toward:
✅ Embracing real-world constraints
✅ Blending domain knowledge + data
✅ Treating robust inference as a learning problem whose objective must be tight with the downstream application for the result of this inference

Here’s what RoPE does:
1️⃣ Uses a small calibration set of real (x, θ) pairs
2️⃣ Learns a correction from simulated to real obs using optimal transport
3️⃣ Enables simulation-based inference you can actually trust

That’s what motivated RoPE, our method being presented at ICML 2025 🎉

RoPE reframes misspecification as a posterior inaccuracy problem, not a simulator/data mismatch in contrast to how model misspecification is often defined in the literature.

Humans design robust statistics by intuition.
Neural SBI doesn’t — unless we teach it how.

🔑 Insight: To make SBI robust, show it real-world data.
And use labeled data to trust the newly learnt inference pipeline.

Why? Because inference method and simulator misspecification are deeply entangled.

🧠 Neural SBI often overfits to quirks in simulators.
🤔 Simpler methods (like ABC with handcrafted stats) often perform better when simulators are slightly wrong.

SBI thrives in ideal settings — but what happens when simulators aren’t perfect?

Real-world practitioners always ask:
“But what if the simulator is off?”

I used to think this was an issue related to the simulator and not to SBI. Now I believe this is the central issue with existing SBI algorithms.

But if my lab can help, please DM me.

AI is just a hoax by big IKEA to sell more bedrooms.

This remains important today. As industry places huge financial bets on large language models, the research community needs to investigate other approaches that may achieve similar or better results via entirely different methods. end/