no code implementations • ICML 2020 • Riccardo Grazzi, Saverio Salzo, Massimiliano Pontil, Luca Franceschi
We study a general class of bilevel optimization problems, in which the upper-level objective is defined via the solution of a fixed point equation.
2 code implementations • 19 Nov 2024 • Riccardo Grazzi, Julien Siems, Jörg K. H. Franke, Arber Zela, Frank Hutter, Massimiliano Pontil
We extend this result to non-diagonal LRNNs, which have recently shown promise in models such as DeltaNet.
1 code implementation • 18 Mar 2024 • Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
In the deterministic case, we provide a linear rate for AID and an improved linear rate for ITD which closely match the ones for the smooth setting.
1 code implementation • 5 Feb 2024 • Riccardo Grazzi, Julien Siems, Simon Schrodi, Thomas Brox, Frank Hutter
State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL), a variant of meta-learning concerning the learned ability to solve tasks during a neural network forward pass, exploiting contextual information provided as input to the model.
1 code implementation • 19 Jul 2023 • Vladimir R. Kostic, Pietro Novelli, Riccardo Grazzi, Karim Lounici, Massimiliano Pontil
We consider the general class of time-homogeneous stochastic dynamical systems, both discrete and continuous, and study the problem of learning a representation of the state that faithfully captures its dynamics.
1 code implementation • 7 Jun 2022 • Riccardo Grazzi, Arya Akhavan, John Isak Texas Falk, Leonardo Cella, Massimiliano Pontil
This is a very strong notion of fairness, since the relative rank is not directly observed by the agent and depends on the underlying reward model and on the distribution of rewards.
2 code implementations • NeurIPS 2023 • Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
We analyse a general class of bilevel problems, in which the upper-level problem consists in the minimization of a smooth objective function and the lower-level problem is to find the fixed point of a smooth contraction map.
no code implementations • 5 Nov 2021 • Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias Seeger, Cedric Archambeau
While classical time series forecasting considers individual time series in isolation, recent advances based on deep learning showed that jointly learning from a large pool of related time series can boost the forecasting accuracy.
no code implementations • 13 Nov 2020 • Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
Bilevel optimization problems are receiving increasing attention in machine learning as they provide a natural framework for hyperparameter optimization and meta-learning.
1 code implementation • 29 Jun 2020 • Riccardo Grazzi, Luca Franceschi, Massimiliano Pontil, Saverio Salzo
We study a general class of bilevel problems, consisting in the minimization of an upper-level objective which depends on the solution to a parametric fixed-point equation.
1 code implementation • 25 Mar 2019 • Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, Massimiliano Pontil
We study the problem of learning-to-learn: inferring a learning algorithm that works well on tasks sampled from an unknown distribution.
2 code implementations • 13 Jun 2018 • Luca Franceschi, Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo, Paolo Frasconi
In (Franceschi et al., 2018) we proposed a unified mathematical framework, grounded on bilevel programming, that encompasses gradient-based hyperparameter optimization and meta-learning.
no code implementations • ICML 2018 • Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, Massimilano Pontil
We introduce a framework based on bilevel programming that unifies gradient-based hyperparameter optimization and meta-learning.