Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning.

Shi Pu, Alex Olshevsky, Ioannis Ch Paschalidis

Abstract read

In one paragraph

Article in IEEE signal processing magazine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent.IEEE transactions on automatic control · 2022
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shi PuInstitute for Data and Decision Analytics, The Chinese University of Hong Kong, Shenzhen, China and Shenzhen Research Institute of Big Data. The research was conducted when the author was with Division of Systems Engineering, Boston University, Boston, MA.

Alex OlshevskyDepartment of Electrical and Computer Engineering and Division of Systems Engineering, Boston University, Boston, MA.

Ioannis Ch PaschalidisDepartment of Electrical and Computer Engineering and Division of Systems Engineering, Boston University, Boston, MA.

Funding

Optimizing and Learning Strategies for Protein Docking R01GM135930 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI VAKILI, PIROOZ · 2019 to 2021

$561k

NIGMS NIH HHS R01 GM135930

6 · The paper itself

Abstract

We provide a discussion of several recent results which, in certain scenarios, are able to overcome a barrier in distributed stochastic optimization for machine learning. Our focus is the so-called asymptotic network independence property, which is achieved whenever a distributed method executed over a network of

Identifiers

PMID33746471

DOI10.1109/msp.2020.2975212

PMCPMC7977622

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.