555 citations · 2.5k across the 105 of their papers we have counts for
5 papers · 2 filters
A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks
Xinyi Wu, Zhengdao Chen, William Wang +1
Oversmoothing is a central challenge of building more powerful Graph Neural Networks (GNNs). While previous works have only demonstrated that oversmoothing is inevitable when the n…
Gradient Descent for Low-Rank Functions
Romain Cosson, Ali Jadbabaie, Anuran Makur +2
Several recent empirical studies demonstrate that important machine learning tasks, e.g., training deep neural networks, exhibit low-rank structure, where the loss function varies…
An Optimal Transport Approach to Personalized Federated Learning
Farzan Farnia, Amirhossein Reisizadeh, Ramtin Pedarsani +1
Federated learning is a distributed machine learning paradigm, which aims to train a model using the local data of many distributed clients. A key challenge in federated learning i…
Byzantine-Robust Federated Linear Bandits
Ali Jadbabaie, Haochuan Li, Jian Qian +1
In this paper, we study a linear bandit optimization problem in a federated setting where a large collection of distributed agents collaboratively learn a common linear bandit mode…
Federated Optimization of Smooth Loss Functions
Ali Jadbabaie, Anuran Makur, Devavrat Shah
In this work, we study empirical risk minimization (ERM) within a federated learning framework, where a central server minimizes an ERM objective function using training data that…