45 citations · 73 across the 6 of their papers we have counts for
6 papers
Conditional Meta-Learning of Linear Representations
Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
Standard meta-learning for representation learning aims to find a common representation to be shared across multiple tasks. The effectiveness of these methods is often limited when…
The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning
Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto
Biased regularization and fine-tuning are two recent meta-learning approaches. They have been shown to be effective to tackle distributions of tasks, in which the tasks' target vec…
Online Parameter-Free Learning of Multiple Low Variance Tasks
Giulia Denevi, Dimitris Stamos, Massimiliano Pontil
We propose a method to learn a common bias vector for a growing sequence of low-variance tasks. Unlike state-of-the-art approaches, our method does not require tuning any hyper-par…
Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi +1
We study the problem of learning-to-learn: inferring a learning algorithm that works well on tasks sampled from an unknown distribution. As class of algorithms we consider Stochast…
Incremental Learning-to-Learn with Statistical Guarantees
Giulia Denevi, Carlo Ciliberto, Dimitris Stamos +1
In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks sampled from an unknown meta distribution. In contrast to previous work on batch…
Iterative algorithms for a non-linear inverse problem in atmospheric lidar
Giulia Denevi, Sara Garbarino, Alberto Sorrentino
We consider the inverse problem of retrieving aerosol extinction coefficients from Raman lidar measurements. In this problem the unknown and the data are related through the expone…