45 citations · 48 across the 3 of their papers we have counts for
3 papers
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…