111 citations · 420 across the 100 of their papers we have counts for
9 papers · 1 filter
Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning
Anis Elgabli, Jihong Park, Amrit S. Bedi +3
In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). To reduce the number of communication li…
Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony
Amrit Singh Bedi, Alec Koppel, Ketan Rajawat +1
An open challenge in supervised learning is \emph{conceptual drift}: a data point begins as classified according to one label, but over time the notion of that label changes. Beyon…
Optimally Compressed Nonparametric Online Learning
Alec Koppel, Amrit Singh Bedi, Ketan Rajawat +1
Batch training of machine learning models based on neural networks is now well established, whereas to date streaming methods are largely based on linear models. To go beyond linea…
Nearly Consistent Finite Particle Estimates in Streaming Importance Sampling
Alec Koppel, Amrit Singh Bedi, Brian M. Sadler +1
In Bayesian inference, we seek to compute information about random variables such as moments or quantiles on the basis of {available data} and prior information. When the distribut…
GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning
Anis Elgabli, Jihong Park, Amrit S. Bedi +2
When the data is distributed across multiple servers, lowering the communication cost between the servers (or workers) while solving the distributed learning problem is an importan…
Adaptive Kernel Learning in Heterogeneous Networks
Hrusikesha Pradhan, Amrit Singh Bedi, Alec Koppel +1
We consider learning in decentralized heterogeneous networks: agents seek to minimize a convex functional that aggregates data across the network, while only having access to their…