13 citations · 38 across the 16 of their papers we have counts for
22 papers
Distributed Stochastic Approximation Algorithms and Heavy-Tailed Age of Information
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
Algorithms in multi-agent systems such as federated learning, mobile robotic swarming, and consensus control can be designed and analyzed as distributed stochastic approximation al…
Approximate Probabilistic Inference for Time-Series Data A Robust Latent Gaussian Model With Temporal Awareness
Anton Johansson, Arunselvan Ramaswamy
The development of robust generative models for highly varied non-stationary time series data is a complex yet important problem. Traditional models for time series data prediction…
A Framework for Provably Stable and Consistent Training of Deep Feedforward Networks
Arunselvan Ramaswamy, Shalabh Bhatnagar, Naman Saxena
We present a novel algorithm for training deep neural networks in supervised (classification and regression) and unsupervised (reinforcement learning) scenarios. This algorithm com…
Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
We generalize the Borkar-Meyn stability Theorem (BMT) to distributed stochastic approximations (SAs) with information delays that possess an arbitrary moment bound. To model the de…
Distributed gradient-based optimization in the presence of dependent aperiodic communication
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
Iterative distributed optimization algorithms involve multiple agents that communicate with each other, over time, in order to minimize/maximize a global objective. In the presence…
3DPG: Distributed Deep Deterministic Policy Gradient Algorithms for Networked Multi-Agent Systems
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
We present Distributed Deep Deterministic Policy Gradient (3DPG), a multi-agent actor-critic (MAAC) algorithm for Markov games. Unlike previous MAAC algorithms, 3DPG is fully distr…