1.4k citations · 18.4k across the 482 of their papers we have counts for
4 papers · 2 filters
Distributed Gradient Flow: Nonsmoothness, Nonconvexity, and Saddle Point Evasion
Brian Swenson, Ryan Murray, H. Vincent Poor +1
The paper considers distributed gradient flow (DGF) for multi-agent nonconvex optimization. DGF is a continuous-time approximation of distributed gradient descent that is often eas…
Malicious Experts versus the multiplicative weights algorithm in online prediction
Erhan Bayraktar, H. Vincent Poor, Xin Zhang
We consider a prediction problem with two experts and a forecaster. We assume that one of the experts is honest and makes correct prediction with probability at each round. The…
Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees
Brian Swenson, Soummya Kar, H. Vincent Poor +2
The article discusses distributed gradient-descent algorithms for computing local and global minima in nonconvex optimization. For local optimization, we focus on distributed stoch…
Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima
Brian Swenson, Ryan Murray, H. Vincent Poor +1
In centralized settings, it is well known that stochastic gradient descent (SGD) avoids saddle points and converges to local minima in nonconvex problems. However, similar guarante…