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Learning the Pareto Front with Hypernetworks
TLDR
The problem of learning the entire Pareto front, with the capability of selecting a desired operating point on the front after training is tackled, and PFL opens the door to new applications where models are selected based on preferences that are only available at run time. Expand
Personalized Federated Learning using Hypernetworks
TLDR
Since hypernetworks share information across clients, it is shown that pFedHN can generalize better to new clients whose distributions differ from any client observed during training, and decouples the communication cost from the trainable model size. Expand
Auxiliary Learning by Implicit Differentiation
TLDR
A novel framework, AuxiLearn, is proposed that targets both challenges of designing useful auxiliary tasks and combining auxiliary tasks into a single coherent loss, based on implicit differentiation. Expand
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
TLDR
This work develops a tree-based hierarchical model in which each internal node of the tree fits a GP to the data using the Pòlya-Gamma augmentation scheme, and shows how the general GP approach achieves improved accuracy on standard incremental few-shot learning benchmarks. Expand
Capturing between-tasks covariance and similarities using multivariate linear mixed models
We consider the problem of predicting several response variables using the same set of explanatory variables. This setting naturally induces a group structure over the coefficient matrix, in whichExpand
Personalized Federated Learning with Gaussian Processes
Federated learning aims to learn a global model that performs well on client devices with limited cross-client communication. Personalized federated learning (PFL) further extends this setup toExpand