4 papers
Federated Ensemble Learning with Progressive Model Personalization
Ala Emrani, Amir Najafi, Abolfazl Motahari
Federated Learning provides a privacy-preserving paradigm for distributed learning, but suffers from statistical heterogeneity across clients. Personalized Federated Learning (PFL)…
Fundamental Limits of Learning High-dimensional Simplices in Noisy Regimes
Seyed Amir Hossein Saberi, Amir Najafi, Abolfazl Motahari +1
In this paper, we establish sample complexity bounds for learning high-dimensional simplices in from noisy data. Specifically, we consider i.i.d. samples uniform…
Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization
Amir Hossein Saberi, Amir Najafi, Ala Emrani +5
The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of $…
Out-Of-Domain Unlabeled Data Improves Generalization
Amir Hossein Saberi, Amir Najafi, Alireza Heidari +3
We propose a novel framework for incorporating unlabeled data into semi-supervised classification problems, where scenarios involving the minimization of either i) adversarially ro…