Erfan Mirzaei

This paves the way for more data-dependent generalization guarantees in dependent-data settings.

Technique highlights:
🔹 Uses blocking methods
🔹 Captures fast-decaying correlations
🔹 Results in tight O(1/n) bounds when decorrelation is fast

Applications:
📊 Covariance operator estimation
🔄 Learning transfer operators for stochastic processes

Our contribution:
We propose empirical Bernstein-type concentration bounds for Hilbert space-valued random variables arising from mixing processes.
🧠 Works for both stationary and non-stationary sequences.

Challenge:
Standard i.i.d. assumptions fail in many learning tasks, especially those involving trajectory data (e.g., molecular dynamics, climate models).
👉 Temporal dependence and slow mixing make it hard to get sharp generalization bounds.

🚨 Poster at #AISTATS2025 tomorrow!
📍Poster Session 1 #125

We present a new empirical Bernstein inequality for Hilbert space-valued random processes—relevant for dependent, even non-stationary data.

w/ Andreas Maurer, @vladimir-slk.bsky.social & M. Pontil

📄 Paper: openreview.net/forum?id=a0E...

1/ 🚀 Over the past two years, our team, CSML, at IIT, has made significant strides in the data-driven modeling of dynamical systems. Curious about how we use advanced operator-based techniques to tackle real-world challenges? Let’s dive in! 🧵👇

An inspiring dive into understanding dynamical processes through 'The Operator Way.' A fascinating approach made accessible for everyone—check it out! 👇👀

Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues Linear Recurrent Neural Networks (LRNNs) such as Mamba, RWKV, GLA, mLSTM, and DeltaNet have emerged as efficient alternatives to Transformers in large language modeling, offering linear scaling with…

Excited to present
"Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues"
at the M3L workshop at #NeurIPS
https://buff.ly/3BlcD4y

If interested, you can attend the presentation the 14th at 15:00, pass at the afternoon poster session, or DM me to discuss :)

In his book “The Nature of Statistical Learning” V. Vapnik wrote:
“When solving a given problem, try to avoid a more general problem as an intermediate step”

Excited to share our lab's amazing contributions at NeurIPS this year! Check out our papers and stay inspired! 🚀📚 #NeurIPS2024

Could add me to the list?

Hi Gaspard. I wonder what you are currently working on in regard to sequence models and world models. Since I have similar interests as you, and in the lab, we had worked on the intersection of the topics (bsky.app/profile/marc...).

Hi 👋 We're glad to be here on @bsky.app and looking forward to engaging in this community. But first, learn a little more about us...

#ELLISforEurope #AI #ML #CrossBorderCollab #PhD