ICML 2026 is underway in Seoul, and I'm delighted that our group has four papers on the program this week: 📍 Tuesday, July 7 · Hall A · Poster #4413 Outcome-Aware Spectral Feature Learning for Instrumental Variable Regression icml.cc/virtual/2026...

Almost 5 years in the making... "Hyperparameter Optimization in Machine Learning" is finally out! 📘 We designed this monograph to be self-contained, covering: Grid, Random & Quasi-random search, Bayesian & Multi-fidelity optimization, Gradient-based methods, Meta-learning. arxiv.org/abs/2410.22854

🚨 OpenReview might have leaked names, but it won't leak the best hyperparameters, unfortunately! 😅 Tired of the drama? Solve your HPO problems before the ICML deadline with this new monograph by our own Luca Franceschi & Massimiliano Pontil (& colleagues). arxiv.org/abs/2410.22854

Hyperparameter Optimization in Machine Learning

Hyperparameters are configuration variables controlling the behavior of machine learning algorithms. They are ubiquitous in machine learning and artificial intelligence and the choice of their values ...

arxiv.org

📢 Upcoming Talk at Our Lab We’re excited to host Arthur Bizzi from EPFL for a research talk next week! Title: Towards Neural Kolmogorov Equations: Parallelizable SDE Learning with Neural PDEs 🗓 Date: November 19 ⏰ Time: 16:00 CET 📍 Galileo Sala, CHT @iitalk.bsky.social

Excited to share our group’s latest work at #AISTATS2025! 🎓 Tackling concentration in dependent data settings with empirical Bernstein bounds for Hilbert space-valued processes. 📍Catch the poster tomorrow! 🔁 See the original tweet for details!

Erfan Mirzaei@erfunmirzaei.bsky.social · last yr.

🚨 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...

DeltaProduct is here! Achieve better state tracing through highly parallel execution. Explore more!🚀

@julien-siems.bsky.social · last yr.

1/9 There is a fundamental tradeoff between parallelizability and expressivity of Large Language Models. We propose a new linear RNN architecture, DeltaProduct, that can effectively navigate this tradeoff. Here's how!

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! 👇👀

Pietro Novelli@pienovelli.bsky.social · 2y ago

For the past four years, I’ve been working on a topic that’s both fascinating and challenging to explain. In this post, I’ve tried to present The Operator Way — a paradigm for understanding dynamical processes — in plain, approachable terms. pietronvll.github.io/the-operator...

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 :)

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…

buff.ly

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”

At #NeurIPS2024 🇨🇦 our group will present 7 contributions! These span a diverse array of topics: from theoretical advances in stochastic processes and reinforcement learning to applications in molecular dynamics and uncertainty quantification.

🎉 I am happy to share that I co-authored my first paper, “Operator World Models for Reinforcement Learning,” published at #NeurIPS2024! 🚀 Glad to present it in Vancouver 🇨🇦. See you there! #AI #ReinforcementLearning @pontilgroup.bsky.social arxiv.org/pdf/2406.19861