221 citations · 609 across the 22 of their papers we have counts for
7 papers · 1 filter
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…
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…
Bounds for Vector-Valued Function Estimation
Andreas Maurer, Massimiliano Pontil
We present a framework to derive risk bounds for vector-valued learning with a broad class of feature maps and loss functions. Multi-task learning and one-vs-all multi-category lea…
The Benefit of Multitask Representation Learning
Andreas Maurer, Massimiliano Pontil, Bernardino Romera-Paredes
We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-lea…
Excess risk bounds for multitask learning with trace norm regularization
Andreas Maurer, Massimiliano Pontil
Trace norm regularization is a popular method of multitask learning. We give excess risk bounds with explicit dependence on the number of tasks, the number of examples per task and…
K-Dimensional Coding Schemes in Hilbert Spaces
Andreas Maurer Massimiliano Pontil
This paper presents a general coding method where data in a Hilbert space are represented by finite dimensional coding vectors. The method is based on empirical risk minimization w…