7 citations · 16 across the 22 of their papers we have counts for
4 papers · 1 filter
Multi-task Bias-Variance Trade-off Through Functional Constraints
Juan Cervino, Juan Andres Bazerque, Miguel Calvo-Fullana +1
Multi-task learning aims to acquire a set of functions, either regressors or classifiers, that perform well for diverse tasks. At its core, the idea behind multi-task learning is t…
Training Graph Neural Networks on Growing Stochastic Graphs
Juan Cervino, Luana Ruiz, Alejandro Ribeiro
Graph Neural Networks (GNNs) rely on graph convolutions to exploit meaningful patterns in networked data. Based on matrix multiplications, convolutions incur in high computational…
Learning Globally Smooth Functions on Manifolds
Juan Cervino, Luiz F. O. Chamon, Benjamin D. Haeffele +2
Smoothness and low dimensional structures play central roles in improving generalization and stability in learning and statistics. This work combines techniques from semi-infinite…
Federated Representation Learning via Maximal Coding Rate Reduction
Juan Cervino, Navid NaderiAlizadeh, Alejandro Ribeiro
We propose a federated methodology to learn low-dimensional representations from a dataset that is distributed among several clients. In particular, we move away from the commonly-…