5 citations · 7 across the 6 of their papers we have counts for
6 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…
Agnostic Learning of Mixed Linear Regressions with EM and AM Algorithms
Avishek Ghosh, Arya Mazumdar
Mixed linear regression is a well-studied problem in parametric statistics and machine learning. Given a set of samples, tuples of covariates and labels, the task of mixed linear r…
An Improved Algorithm for Clustered Federated Learning
Harshvardhan, Avishek Ghosh, Arya Mazumdar
In this paper, we address the dichotomy between heterogeneous models and simultaneous training in Federated Learning (FL) via a clustering framework. We define a new clustering mod…
Adaptive Clustering and Personalization in Multi-Agent Stochastic Linear Bandits
Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran
We consider the problem of minimizing regret in an agent heterogeneous stochastic linear bandits framework, where the agents (users) are similar but not all identical. We model…