Eiko Yoneki

Eiko Yoneki

University of Cambridge

H-index: 34

Europe-United Kingdom

About Eiko Yoneki

Eiko Yoneki, With an exceptional h-index of 34 and a recent h-index of 19 (since 2020), a distinguished researcher at University of Cambridge, specializes in the field of optimisation, large-scale graph processing, distributed systems.

His recent articles reflect a diverse array of research interests and contributions to the field:

SIP: Autotuning GPU Native Schedules via Stochastic Instruction Perturbation

Guided Graph Generation: Evaluation of Graph Generators in Terms of Network Statistics, and a New Algorithm

Best of both, Structured and Unstructured Sparsity in Neural Networks

MCTS-GEB: Monte Carlo Tree Search is a Good E-graph Builder

X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation

RLFlow: Optimising Neural Network Subgraph Transformation with World Models

BoGraph: structured Bayesian optimization from logs for expensive systems with many parameters

High-dimensional bayesian optimization with multi-task learning for rocksdb

Eiko Yoneki Information

University

Position

Computer Laboratory

Citations(all)

6782

Citations(since 2020)

1622

Cited By

6101

hIndex(all)

34

hIndex(since 2020)

19

i10Index(all)

61

i10Index(since 2020)

29

Email

University Profile Page

University of Cambridge

Google Scholar

View Google Scholar Profile

Eiko Yoneki Skills & Research Interests

optimisation

large-scale graph processing

distributed systems

Top articles of Eiko Yoneki

Title

Journal

Author(s)

Publication Date

SIP: Autotuning GPU Native Schedules via Stochastic Instruction Perturbation

arXiv preprint arXiv:2403.16863

Guoliang He

Eiko Yoneki

2024/3/25

Guided Graph Generation: Evaluation of Graph Generators in Terms of Network Statistics, and a New Algorithm

arXiv preprint arXiv:2303.00635

Jérôme Kunegis

Jun Sun

Eiko Yoneki

2023/3/1

Best of both, Structured and Unstructured Sparsity in Neural Networks

Christoph Schulte

Sven Wagner

Armin Runge

Dimitrios Bariamis

Barbara Hammer

2023/5/8

MCTS-GEB: Monte Carlo Tree Search is a Good E-graph Builder

Guoliang He

Zak Singh

Eiko Yoneki

2023/5/8

X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation

Proceedings of Machine Learning and Systems

Guoliang He

Sean Parker

Eiko Yoneki

2023/3/18

RLFlow: Optimising Neural Network Subgraph Transformation with World Models

arXiv preprint arXiv:2205.01435

Sean Parker

Sami Alabed

Eiko Yoneki

2022/5/3

BoGraph: structured Bayesian optimization from logs for expensive systems with many parameters

Sami Alabed

Eiko Yoneki

2022/4/5

High-dimensional bayesian optimization with multi-task learning for rocksdb

Sami Alabed

Eiko Yoneki

2021/4/26

The future is big graphs: a community view on graph processing systems

Communications of the ACM

Sherif Sakr

Angela Bonifati

Hannes Voigt

Alexandru Iosup

Khaled Ammar

...

2021/8/24

GDDR: GNN-based data-driven routing

Oliver Hope

Eiko Yoneki

2021/7/7

World-models for bitrate streaming

Applied Sciences

Harrison Brown

Kai Fricke

Eiko Yoneki

2020/9/24

Collaborative facilitation and collaborative inhibition in virtual environments

Future Internet

Andrea Guazzini

Elisa Guidi

Cristina Cecchini

Eiko Yoneki

2020/7/13

Performance analysis of single board computer clusters

Future Generation Computer Systems

Philip J Basford

Steven J Johnston

Colin S Perkins

Tony Garnock-Jones

Fung Po Tso

...

2020/1/1

See List of Professors in Eiko Yoneki University(University of Cambridge)

Co-Authors

H-index: 101
Jon Crowcroft

Jon Crowcroft

University of Cambridge

H-index: 51
José F.F. Mendes

José F.F. Mendes

Universidade de Aveiro

H-index: 46
Richard Mortier

Richard Mortier

University of Cambridge

H-index: 41
Steven Hand

Steven Hand

University of Cambridge

H-index: 39
Hyoungshick Kim

Hyoungshick Kim

Sungkyunkwan University

H-index: 35
Eamonn O'Neill

Eamonn O'Neill

University of Bath

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