85 citations · 236 across the 13 of their papers we have counts for
4 papers · 1 filter
MARTHE: Scheduling the Learning Rate Via Online Hypergradients
Michele Donini, Luca Franceschi, Massimiliano Pontil +2
We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization, aiming at good generalization. We describe the structure…
Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis
Cristina Cornelio, Michele Donini, Andrea Loreggia +2
In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep…
Learning Fair and Transferable Representations
Luca Oneto, Michele Donini, Andreas Maurer +1
Developing learning methods which do not discriminate subgroups in the population is a central goal of algorithmic fairness. One way to reach this goal is by modifying the data rep…
General Fair Empirical Risk Minimization
Luca Oneto, Michele Donini, Massimiliano Pontil
We tackle the problem of algorithmic fairness, where the goal is to avoid the unfairly influence of sensitive information, in the general context of regression with possible contin…