About Me

I like to study and design efficient and scalable machine learning methods. My main focus is on state-tracking and quantization for LLMs. I am a Researcher at Microsoft Cambrdige, and was a Post Doc at IIT in Genoa, and before that a PhD Student at IIT and UCL.

News

[Mar 2025] “DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products” as been accepted as an Oral at the ICLR 2025 workshop on Foundation Models in the Wild.
[Feb 2025] “Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues” has been accepted as an Oral at ICLR 2025.
[Jan 2025] I gave two online seminars on “Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues”. FLANN Seminar, with a stronger focus on theory. AutoML Seminar, featuring Julien Siems, with a stronger focus on experiments.
[Oct 2024] “Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues” has been accepted at the NeurIPS 2024 workshop Mathematics of Modern Machine Learning (M3L).
[Oct 2024] “Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues” has been accepted at the NeurIPS 2024 workshop Mathematics of Modern Machine Learning (M3L).
[May 2024] “Nonsmooth Implicit Differentiation: Deterministic and Stochastic Convergence Rates” has been accepted at ICML 2024.
[May 2024] “Is Mamba Capable of In-Context Learning?” has been accepted at the conference on AutoML.

Seminars and Lectures

[March 2025] State Tracking in Scalable Linear RNNs, ASAP Online Seminar (Youtube Link)
[January 2025] Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues, FLANN Online Seminar (Youtube Link)
[January 2025] Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues, AutoML Online Seminar (Youtube Link)
[April 2024] Is Mamba Capable of In-Context Learning?, AutoML Online Seminar (YouTube link) (Extended Slides)
[August 2023] Principled and Efficient Bilevel Optimization for Machine Learning, Lecture at the Bilevel Summer School (Slides) (School Program)

Publications

Arxiv

DeltaProduct: Increasing the Expressivity of DeltaNet Through Products of Householders

Julien Siems, Timur Carstensen, Arber Zela, Frank Hutter, Massimiliano Pontil, Riccardo Grazzi

Arxiv, 2025

PDF

ICLR (Oral)

Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues

Riccardo Grazzi, Julien Siems, Jörg K.H. Franke, Arber Zela, Frank Hutter, Massimiliano Pontil

International Conference on Learning Representations, 2025.

PDF Code

ICML

Nonsmooth Implicit Differentiation, Deterministic and Stochastic Convergence Rates

Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo

International Conference of Machine Learning, 2024.

PDF Code

AutoML

Is Mamba Capable of In-Context Learning?

Riccardo Grazzi*, Julien Siems*, Simon Schrodi, Thomas Brox, Frank Hutter (*equal contribution)

International Conference on Automated Machine Learning, 2024.

PDF Code

ICLR

Learning invariant representations of time-homogeneous stochastic dynamical systems

Vladimir R. Kostic, Pietro Novelli, Riccardo Grazzi, Karim Lounici, Massimiliano Pontil

International Conference on Learning Representations, 2024.

PDF Code Project Page

JMLR

Bilevel Optimization with a Lower-level Contraction: Optimal Sample Complexity without Warm-start

Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo

Journal of Machine Learning Research, 2023.

PDF Code

NeurIPS

Group Meritocratic Fairness in Linear Contextual Bandits

Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk, Leonardo Cella, Massimiliano Pontil

Neural Information Processing Systems, 2022.

PDF Code

Arxiv

Meta-Forecasting by combining Global Deep Representations with Local Adaptation

Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias Seeger, Cedric Archambeau

PDF

AISTATS

Convergence Properties of Stochastic Hypergradients

Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo

International Conference on Artificial Intelligence and Statistics, 2021.

PDF

ICML

On the Iteration Complexity of Hypergradient Computation

Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo

Intenrnational Conference of Machine Learning, 2020.

PDF Code

ICML

Learning-to-Learn Stochastic Gradient Descent with Biased Regularization

Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil

Intenrnational Conference of Machine Learning, 2019.

PDF Code

ICML

Bilevel Programming for Hyperparameter Optimization and Meta-Learning

Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimilano Pontil

Intenrnational Conference of Machine Learning, 2018.

PDF Code

AutoML

Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning

Luca Franceschi, Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo, Paolo Frasconi

AutoML Workshop, ICML 2018

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