We stand with Ukraine

We stand with Ukraine

Shi Pu

Orcid: 0000-0002-5813-527X

Affiliations:

Chinese University of Hong Kong, Shenzhen, Guangdong, China

According to our database¹, Shi Pu authored at least 33 papers between 2016 and 2026.

Collaborative distances:

Dijkstra number² of five.
Erdős number³ of four.

Timeline

Legend:

Book In proceedings Article PhD thesis Dataset Other

Links

Online presence:

on orcid.org

On csauthors.net:

Bibliography

2026

Achieving Linear Speedup for Composite Federated Learning.

[DOI]

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CoRR, February, 2026

Correction: An accelerated distributed stochastic gradient method with momentum.

[DOI]

,

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Math. Program., January, 2026

An accelerated distributed stochastic gradient method with momentum.

[DOI]

,

,

Math. Program., January, 2026

Stochastic Push-Pull for Decentralized Nonconvex Optimization.

[DOI]

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IEEE Trans. Signal Process., 2026

2025

FedSUM Family: Efficient Federated Learning Methods under Arbitrary Client Participation.

[DOI]

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CoRR, December, 2025

Distributed Random Reshuffling Methods With Improved Convergence.

[DOI]

,

,

IEEE Trans. Autom. Control., September, 2025

CEDAS: A Compressed Decentralized Stochastic Gradient Method With Improved Convergence.

[DOI]

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IEEE Trans. Autom. Control., April, 2025

Distributed Learning over Arbitrary Topology: Linear Speed-Up with Polynomial Transient Time.

[DOI]

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CoRR, March, 2025

2024

Distributed Stochastic Optimization Under a General Variance Condition.

[DOI]

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,

IEEE Trans. Autom. Control., September, 2024

Optimal gradient tracking for decentralized optimization.

[DOI]

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,

,

Math. Program., September, 2024

Provably Accelerated Decentralized Gradient Methods Over Unbalanced Directed Graphs.

[DOI]

,

,

,

SIAM J. Optim., March, 2024

B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data.

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Improving the Transient Times for Distributed Stochastic Gradient Methods.

[DOI]

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IEEE Trans. Autom. Control., July, 2023

Distributed Random Reshuffling Over Networks.

[DOI]

,

,

,

,

IEEE Trans. Signal Process., 2023

Private and Accurate Decentralized Optimization via Encrypted and Structured Functional Perturbation.

[DOI]

,

IEEE Control. Syst. Lett., 2023

2022

Compressed Gradient Tracking for Decentralized Optimization Over General Directed Networks.

[DOI]

,

,

,

IEEE Trans. Signal Process., 2022

A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent.

[DOI]

,

,

Ioannis Ch. Paschalidis

IEEE Trans. Autom. Control., 2022

A Compressed Gradient Tracking Method for Decentralized Optimization With Linear Convergence.

[DOI]

,

,

,

IEEE Trans. Autom. Control., 2022

2021

Push-Pull Gradient Methods for Distributed Optimization in Networks.

[DOI]

,

,

,

IEEE Trans. Autom. Control., 2021

Distributed stochastic gradient tracking methods.

[DOI]

,

Math. Program., 2021

Provably Accelerated Decentralized Gradient Method Over Unbalanced Directed Graphs.

[DOI]

,

,

,

CoRR, 2021

2020

An Online Mechanism for Resource Allocation in Networks.

[DOI]

,

José Joaquín Escudero Garzás

,

Alfredo García

,

Shahin Shahrampour

IEEE Trans. Control. Netw. Syst., 2020

Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning: Examining Distributed and Centralized Stochastic Gradient Descent.

[DOI]

,

,

Ioannis Ch. Paschalidis

IEEE Signal Process. Mag., 2020

A General Framework for Decentralized Optimization With First-Order Methods.

[DOI]

,

,

,

Proc. IEEE, 2020

A Robust Gradient Tracking Method for Distributed Optimization over Directed Networks.

[DOI]

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019

Asymptotic Network Independence in Distributed Optimization for Machine Learning.

[DOI]

,

Ioannis Ch. Paschalidis

,

CoRR, 2019

A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient Descent.

[DOI]

,

Ioannis Ch. Paschalidis

,

CoRR, 2019

2018

Erratum: Swarming for Faster Convergence in Stochastic Optimization.

[DOI]

,

Alfredo García

SIAM J. Control. Optim., 2018

Swarming for Faster Convergence in Stochastic Optimization.

[DOI]

,

Alfredo García

SIAM J. Control. Optim., 2018

A Flocking-Based Approach for Distributed Stochastic Optimization.

[DOI]

,

Alfredo García

Oper. Res., 2018

A Push-Pull Gradient Method for Distributed Optimization in Networks.

[DOI]

,

,

,

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

A Distributed Stochastic Gradient Tracking Method.

[DOI]

,

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2016

Noise Reduction by Swarming in Social Foraging.

[DOI]

,

Alfredo García

,

IEEE Trans. Autom. Control., 2016

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