4 papers · 1 filter
On the Theory of Continual Learning with Gradient Descent for Neural Networks
Hossein Taheri, Avishek Ghosh, Arya Mazumdar
Continual learning, the ability of a model to adapt to an ongoing sequence of tasks without forgetting earlier ones, is a central goal of artificial intelligence. To better underst…
LocalKMeans: Convergence of Lloyd's Algorithm with Distributed Local Iterations
Harsh Vardhan, Heng Zhu, Avishek Ghosh +1
In this paper, we analyze the classical -means alternating-minimization algorithm, also known as Lloyd's algorithm (Lloyd, 1956), for a mixture of Gaussians in a data-distribute…
Learning and Generalization with Mixture Data
Harsh Vardhan, Avishek Ghosh, Arya Mazumdar
In many, if not most, machine learning applications the training data is naturally heterogeneous (e.g. federated learning, adversarial attacks and domain adaptation in neural net t…
Client Selection in Federated Learning with Data Heterogeneity and Network Latencies
Harsh Vardhan, Xiaofan Yu, Tajana Rosing +1
Federated learning (FL) is a distributed machine learning paradigm where multiple clients conduct local training based on their private data, then the updated models are sent to a…