160 citations · 618 across the 56 of their papers we have counts for
6 papers · 1 filter
Anytime Minibatch with Delayed Gradients
Haider Al-Lawati, Stark C. Draper
Distributed optimization is widely deployed in practice to solve a broad range of problems. In a typical asynchronous scheme, workers calculate gradients with respect to out-of-dat…
Astronomical Image Quality Prediction based on Environmental and Telescope Operating Conditions
Sankalp Gilda, Yuan-Sen Ting, Kanoa Withington +6
Intelligent scheduling of the sequence of scientific exposures taken at ground-based astronomical observatories is massively challenging. Observing time is over-subscribed and atmo…
Node-Centric Graph Learning from Data for Brain State Identification
Nafiseh Ghoroghchian, David M. Groppe, Roman Genov +2
Data-driven graph learning models a network by determining the strength of connections between its nodes. The data refers to a graph signal which associates a value with each graph…
A Hierarchical Graph Signal Processing Approach to Inference from Spatiotemporal Signals
Nafiseh Ghoroghchian, Stark C. Draper, Roman Genov
Motivated by the emerging area of graph signal processing (GSP), we introduce a novel method to draw inference from spatiotemporal signals. Data acquisition in different locations…
Distributed Coding of Quantized Random Projections
Maxim Goukhshtein, Petros T. Boufounos, Toshiaki Koike-Akino +1
In this paper we propose a new framework for distributed source coding of structured sources, such as sparse signals. Our framework capitalizes on recent advances in the theory of…
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization
Nuwan Ferdinand, Haider Al-Lawati, Stark C. Draper +1
Distributed optimization is vital in solving large-scale machine learning problems. A widely-shared feature of distributed optimization techniques is the requirement that all nodes…