639 citations · 2.9k across the 94 of their papers we have counts for
6 papers · 2 filters
Taking Advantage of Multitask Learning for Fair Classification
Luca Oneto, Michele Donini, Amon Elders +1
A central goal of algorithmic fairness is to reduce bias in automated decision making. An unavoidable tension exists between accuracy gains obtained by using sensitive information…
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo +2
We introduce a framework based on bilevel programming that unifies gradient-based hyperparameter optimization and meta-learning. We show that an approximate version of the bilevel…
Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
Giulia Luise, Alessandro Rudi, Massimiliano Pontil +1
Applications of optimal transport have recently gained remarkable attention thanks to the computational advantages of entropic regularization. However, in most situations the Sinkh…
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…
Empirical bounds for functions with weak interactions
Andreas Maurer, Massimiliano Pontil
We provide sharp empirical estimates of expectation, variance and normal approximation for a class of statistics whose variation in any argument does not change too much when anoth…
Empirical Risk Minimization under Fairness Constraints
Michele Donini, Luca Oneto, Shai Ben-David +2
We address the problem of algorithmic fairness: ensuring that sensitive variables do not unfairly influence the outcome of a classifier. We present an approach based on empirical r…