GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning | Paper Citation Record

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GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning write newline

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GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning 4" FUNCTION default.is.dash.repeated.names #1 FUNCTION default.name.format.string

Reference 2

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GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning write newline

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GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning write newline

Reference 4

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GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning write newline

Reference 5

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GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning write newline

Reference 6

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This paper cites Distributed large-scale natural graph factorization.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Distributed large-scale natural graph factorization

Reference 7

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This paper cites Asynchronous saddle point algorithm for stochastic optimization in heterogeneous networks.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Asynchronous saddle point algorithm for stochastic optimization in heterogeneous networks

Reference 8

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This paper cites A convergent incremental gradient method with a constant step size.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning A convergent incremental gradient method with a constant step size

Reference 9

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Observation 16c8cb47-05dc-4ca4-9670-a1ebf4a4cd23 · outbound

This paper cites The n-city travelling salesman problem: Statistical mechanics and the metropolis algorithm.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning The n-city travelling salesman problem: Statistical mechanics and the metropolis algorithm

Reference 10

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Observation 02f65f40-9b2a-42dd-a947-8167da1defd2 · outbound

This paper cites Distributed optimization and statistical learning via the alternating direction method of multipliers.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Distributed optimization and statistical learning via the alternating direction method of multipliers

Reference 11

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This paper cites Multi-agent distributed optimization via inexact consensus admm.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Multi-agent distributed optimization via inexact consensus admm

Reference 12

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This paper cites Distributed constrained optimization by consensus-based primal-dual perturbation method.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Distributed constrained optimization by consensus-based primal-dual perturbation method

Reference 13

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Observation 309be269-3a3a-4700-87c7-a937b5c9d461 · outbound

This paper cites The direct extension of admm for multi-block convex minimization problems is not necessarily convergent.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning The direct extension of admm for multi-block convex minimization problems is not necessarily convergent

Reference 14

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Observation 23c8ab33-3a3f-4fe7-87df-14f43501f744 · outbound

This paper cites Lag: Lazily aggregated gradient for communication-efficient distributed learning.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Lag: Lazily aggregated gradient for communication-efficient distributed learning

Reference 15

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Observation f8f07e28-b1ad-440c-91d0-e0844abcc0e1 · outbound

This paper cites Large scale distributed deep networks.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Large scale distributed deep networks

Reference 16

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Observation fadf73a8-bca8-461c-a3b4-25b57de6e4fc · outbound

This paper cites Parallel multi-block admm with o(1/k) convergence.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Parallel multi-block admm with o(1/k) convergence

Reference 17

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Observation 88f9bd00-3d44-4b56-9bd3-e2f33d8d1fc4 · outbound

This paper cites Ant colonies for the travelling salesman problem.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Ant colonies for the travelling salesman problem

Reference 18

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Observation 3db7c6d1-9ea4-4ed0-9e57-a565c848b427 · outbound

This paper cites UCI machine learning repository, 2017.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning UCI machine learning repository, 2017

Reference 19

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This paper cites Dual averaging for distributed optimization: Convergence analysis and network scaling.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Dual averaging for distributed optimization: Convergence analysis and network scaling

Reference 20

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Observation eead9c10-c3ad-4a06-a96f-c7b99ec47ffc · outbound

This paper cites A dual algorithm for the solution of non linear variational problems via finite element approximation.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning A dual algorithm for the solution of non linear variational problems via finite element approximation

Reference 21

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This paper cites Sur l'approximation, par \'e l \'e ments finis d'ordre un, et la r \'e solution, par p \'e nalisation-dualit \'e d'une classe de probl \`e mes de dirichlet non lin \'e aires.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Sur l'approximation, par \'e l \'e ments finis d'ordre un, et la r \'e solution, par p \'e nalisation-dualit \'e d'une classe de probl \`e mes de dirichlet non lin \'e aires

Reference 22

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Observation 808ac13e-0716-42b4-89ff-cb5cbd48ce2c · outbound

This paper cites On the convergence rate of incremental aggregated gradient algorithms.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning On the convergence rate of incremental aggregated gradient algorithms

Reference 23

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This paper cites A class of projection and contraction methods for monotone variational inequalities.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning A class of projection and contraction methods for monotone variational inequalities

Reference 24

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This paper cites On the o(1/n) convergence rate of the douglas--rachford alternating direction method.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning On the o(1/n) convergence rate of the douglas--rachford alternating direction method

Reference 25

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Observation c9e92aec-3b30-403c-9e0f-76c3ab7baf77 · outbound

This paper cites On non-ergodic convergence rate of douglas--rachford alternating direction method of multipliers.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning On non-ergodic convergence rate of douglas--rachford alternating direction method of multipliers

Reference 26

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This paper cites On full jacobian decomposition of the augmented lagrangian method for separable convex programming.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning On full jacobian decomposition of the augmented lagrangian method for separable convex programming

Reference 27

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Observation a3359784-45a0-4b4e-9d2a-efe0ab89596d · outbound

This paper cites Cola: Decentralized linear learning.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Cola: Decentralized linear learning

Reference 28

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This paper cites Communication-efficient distributed dual coordinate ascent.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Communication-efficient distributed dual coordinate ascent

Reference 29

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Observation 63dac667-db8f-48a5-a33f-5fb6f9811cd4 · outbound

This paper cites Fast distributed gradient methods.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Fast distributed gradient methods

Reference 30

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This paper cites Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

Reference 31

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This paper cites Jordan, Jason D.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Jordan, Jason D

Reference 32

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This paper cites Proximity without consensus in online multiagent optimization.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Proximity without consensus in online multiagent optimization

Reference 33

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This paper cites Communication-efficient algorithms for decentralized and stochastic optimization.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Communication-efficient algorithms for decentralized and stochastic optimization

Reference 34

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This paper cites Some simple applications of the travelling salesman problem.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Some simple applications of the travelling salesman problem

Reference 35

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This paper cites Distributed delayed proximal gradient methods.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Distributed delayed proximal gradient methods

Reference 36

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This paper cites Communication efficient distributed machine learning with the parameter server.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Communication efficient distributed machine learning with the parameter server

Reference 37

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This paper cites Splitting algorithms for the sum of two nonlinear operators.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Splitting algorithms for the sum of two nonlinear operators

Reference 38

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Observation 96c8a6b3-1c1f-420e-8b86-6877025b17ff · outbound

This paper cites Communication-censored ADMM for decentralized consensus optimization.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Communication-censored ADMM for decentralized consensus optimization

Reference 39

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Observation 9997d538-84d4-4572-8c52-a1192e8336c3 · outbound

This paper cites Distributed optimization with arbitrary local solvers.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Distributed optimization with arbitrary local solvers

Reference 40

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Observation 6c9b92c3-d3da-422b-a2e5-7bdc963385d4 · outbound

This paper cites On the capacity of channels with gaussian and non-gaussian noise.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning On the capacity of channels with gaussian and non-gaussian noise

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:46.046283Z

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This paper cites Brendan McMahan, Ramage Daniel Moore, Eider, Seth Hampson, and Blaise Ag\" u era yArcas.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Brendan McMahan, Ramage Daniel Moore, Eider, Seth Hampson, and Blaise Ag\" u era yArcas

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:46.023168Z

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Observation 6ee10588-57af-47f4-9785-fbcfce8efcc7 · outbound

This paper cites Distributed optimization over time-varying directed graphs.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Distributed optimization over time-varying directed graphs

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.993502Z

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Observation c917c8a7-3541-4859-85c4-6d49f258349c · outbound

This paper cites Distributed subgradient methods for multi-agent optimization.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Distributed subgradient methods for multi-agent optimization

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.968774Z

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Observation 0f0f78a6-c03d-4852-b6cb-858ef89ca73b · outbound

This paper cites Achieving geometric convergence for distributed optimization over time-varying graphs.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Achieving geometric convergence for distributed optimization over time-varying graphs

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.948195Z

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Observation ac38fe59-2d09-4724-830b-0e819a9091b3 · outbound

This paper cites Network topology and communication-computation tradeoffs in decentralized optimization.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Network topology and communication-computation tradeoffs in decentralized optimization

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.920639Z

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Observation 5491f3eb-ed02-4b9b-abba-b3d2375f1768 · outbound

This paper cites Wireless network intelligence at the edge.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Wireless network intelligence at the edge

Reference 47

Resolution: verified exact; raw_fallback, observed 2026-08-14T10:09:45.579589Z

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Observation e16c9d4e-6658-41a4-b58a-13dc5b3c2783 · outbound

This paper cites Parallel distributed approaches to combinatorial optimization: benchmark studies on traveling salesman problem.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Parallel distributed approaches to combinatorial optimization: benchmark studies on traveling salesman problem

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.900760Z

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Observation 99aa0d7b-3b8d-4e17-89f1-2dfc382207c3 · outbound

This paper cites Optimal algorithms for non-smooth distributed optimization in networks.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Optimal algorithms for non-smooth distributed optimization in networks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.876356Z

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Observation 4da30dbd-a09b-4583-8c5d-c4ca1f5a7b1b · outbound

This paper cites Minimizing finite sums with the stochastic average gradient.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Minimizing finite sums with the stochastic average gradient

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.858093Z

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Observation b0367b8d-040a-4dd1-95e9-3e21b04a40c5 · outbound

This paper cites A proximal gradient algorithm for decentralized composite optimization.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning A proximal gradient algorithm for decentralized composite optimization

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.838545Z

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Observation 433f4319-1f9d-48b5-bf89-d8337a7cf5c6 · outbound

This paper cites Murthy, and Vaneet Aggarwal.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Murthy, and Vaneet Aggarwal

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.817542Z

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Observation a97f21f9-5c45-42b5-98f9-5c1faf84119f · outbound

This paper cites Distributed mean estimation with limited communication.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Distributed mean estimation with limited communication

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.796751Z

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Observation a6d87713-bd8b-4884-ab98-45dddc9ac8ba · outbound

This paper cites Distributed consensus over network with noisy links.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Distributed consensus over network with noisy links

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.777155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 525f9234-a96b-4d5c-9de0-75c9d218bb47 · outbound

This paper cites Tsianos, Sean Lawlor, and Michael G.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Tsianos, Sean Lawlor, and Michael G

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.755966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dac8f618-b10a-4c6a-9664-f8d591a731c8 · outbound

This paper cites Parallel direction method of multipliers.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Parallel direction method of multipliers

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.737909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 11a1f52d-39f4-4221-ae3b-d8f78b064d21 · outbound

This paper cites Group-based alternating direction method of multipliers for distributed linear classification.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Group-based alternating direction method of multipliers for distributed linear classification

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.719305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 09c76f40-5f39-465d-855c-785666069f0b · outbound

This paper cites Adaptive Federated Learning in Resource Constrained Edge Computing Systems.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Reference 58

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T10:09:45.436933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b2ddf739-49b0-4c75-a581-bdb1431a28ec · outbound

This paper cites Communication-efficient algorithms for statistical optimization.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Communication-efficient algorithms for statistical optimization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.701796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eb3f0088-d2f3-4b8a-ad1e-d16bcd563042 · outbound

This paper cites Quantized consensus ADMM for multi-agent distributed optimization.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning Quantized consensus ADMM for multi-agent distributed optimization

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:09:45.681649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Reference resolution

Outbound references

Pith citing papers