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Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions
Hossein Zakerinia, Jonathan Scott, Christoph H. Lampert
Personalized federated learning has emerged as a popular approach to training on devices holding statistically heterogeneous data, known as clients. However, most existing approach…
Fast Rate Bounds for Multi-Task and Meta-Learning with Different Sample Sizes
Hossein Zakerinia, Christoph H. Lampert
We present new fast-rate PAC-Bayesian generalization bounds for multi-task and meta-learning in the unbalanced setting, i.e. when the tasks have training sets of different sizes, a…
From Low Intrinsic Dimensionality to Non-Vacuous Generalization Bounds in Deep Multi-Task Learning
Hossein Zakerinia, Dorsa Ghobadi, Christoph H. Lampert
Deep learning methods are known to generalize well from training to future data, even in an overparametrized regime, where they could easily overfit. One explanation for this pheno…