Rethinking Bayesian learning for data analysis: The art of prior and inference in sparsity-aware modeling
Sparse modeling for signal processing and machine learning, in general, has been at the
focus of scientific research for over two decades. Among others, supervised sparsity-aware …
focus of scientific research for over two decades. Among others, supervised sparsity-aware …
Federated learning over wireless device-to-device networks: Algorithms and convergence analysis
The proliferation of Internet-of-Things (IoT) devices and cloud-computing applications over
siloed data centers is motivating renewed interest in the collaborative training of a shared …
siloed data centers is motivating renewed interest in the collaborative training of a shared …
Nonconvex min-max optimization: Applications, challenges, and recent theoretical advances
The min-max optimization problem, also known as the<; i> saddle point problem<;/i>, is a
classical optimization problem that is also studied in the context of zero-sum games. Given a …
classical optimization problem that is also studied in the context of zero-sum games. Given a …
Decentralized policy gradient descent ascent for safe multi-agent reinforcement learning
This paper deals with distributed reinforcement learning problems with safety constraints. In
particular, we consider that a team of agents cooperate in a shared environment, where …
particular, we consider that a team of agents cooperate in a shared environment, where …
Decentralized federated learning via SGD over wireless D2D networks
Federated Learning (FL), an emerging paradigm for fast intelligent acquisition at the network
edge, enables joint training of a machine learning model over distributed data sets and …
edge, enables joint training of a machine learning model over distributed data sets and …
A survey of distributed optimization methods for multi-robot systems
Distributed optimization consists of multiple computation nodes working together to minimize
a common objective function through local computation iterations and network-constrained …
a common objective function through local computation iterations and network-constrained …
Dinno: Distributed neural network optimization for multi-robot collaborative learning
We present DiNNO, a distributed algorithm that enables a group of robots to collaboratively
optimize a deep neural network model while communicating over a mesh network. Each …
optimize a deep neural network model while communicating over a mesh network. Each …
Fast decentralized nonconvex finite-sum optimization with recursive variance reduction
This paper considers decentralized minimization of N:=nm smooth nonconvex cost functions
equally divided over a directed network of n nodes. Specifically, we describe a stochastic …
equally divided over a directed network of n nodes. Specifically, we describe a stochastic …
Decentralized stochastic gradient tracking for non-convex empirical risk minimization
This paper studies a decentralized stochastic gradient tracking (DSGT) algorithm for non-
convex empirical risk minimization problems over a peer-to-peer network of nodes, which is …
convex empirical risk minimization problems over a peer-to-peer network of nodes, which is …
Federated generalized bayesian learning via distributed stein variational gradient descent
This paper introduces Distributed Stein Variational Gradient Descent (DSVGD), a non-
parametric generalized Bayesian inference framework for federated learning. DSVGD …
parametric generalized Bayesian inference framework for federated learning. DSVGD …