QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
2020 • 4208 Citations
Published in The 35th International Conference on Machine Learning (ICML 2018)
Shimon Whiteson
Shimon Whiteson is a researcher affiliated with Professor of Computer Science, University of Oxford / Senior Staff Research Scientist, Waymo, specializing in AI & Machine Learning. Their work has been cited 33,343 times. This profile visualizes their global influence, highlighting strong citation networks in CN.
AI & Machine Learning
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Professor of Computer Science, University of Oxford / Senior Staff Research Scientist, Waymo•
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Shimon Whiteson's Citation Metrics
Bibliometric impact based on 1 indexed publication.
- H-Index
- 1
- i10-Index
- 1
- Total Citations
- 33,343
- Citing Countries
- 6
As of April 2026.
Shimon Whiteson has an h-index of 1 and 33,343 total citations across 1 publication, with research cited by institutions in 6 countries.
Global Impact Map
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Top Citing Countries(Limited)
China(CN)
8Singapore(SG)
5United States(US)
3United Kingdom(GB)
2Germany(DE)
2Canada(CA)
1Top Citing Institutions(Limited)
Tsinghua University
4Nanyang Technological University
4University of California, Berkeley
3Jilin University
2Technical University of Munich
2Tongji University
1King's College London
1University College London
1Singapore University of Technology and Design
1Peking University
1Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Citer Influence Network
Visa-ready visualisation: each outer ring node is a country citing this scholar. Node size & edge weight scale with citing-paper count. Showing top 6 of 6 countries (21 total citations).
Letter of Support — Candidate Recommenders
Ranked by EB-1A / O-1A relevance: independence from your home institution, affiliation with a top-ranked institution, repeat citations of your work, and recency. Reach out to the highest-scoring candidates to request support letters.
- #1
Dusit Niyato
7.0Nanyang Technological University
Singapore· Cited your work 1 time
IndependentTop institution - #2
Zeqiang Liew
7.0Nanyang Technological University
Singapore· Cited your work 1 time
IndependentTop institution - #3
Jiaxuan Gao
7.0Tsinghua University
China· Cited your work 1 time
IndependentTop institution - #4
Yu Wang
7.0Tsinghua University
China· Cited your work 1 time
IndependentTop institution - #5
Eugene Vinitsky
7.0University of California, Berkeley
United States· Cited your work 1 time
IndependentTop institution - #6
Akash Velu
7.0University of California, Berkeley
United States· Cited your work 1 time
IndependentTop institution - #7
Alois C. Knoll
7.0Technical University of Munich
Germany· Cited your work 1 time
IndependentTop institution - #8
Florian Walter
7.0Technical University of Munich
Germany· Cited your work 1 time
IndependentTop institution - #9
Yali Du
7.0King's College London
UK· Cited your work 1 time
IndependentTop institution - #10
Jun Wang
7.0University College London
UK· Cited your work 1 time
IndependentTop institution
Scores combine 5 signals: independence (+3), prestige institution (+2), repeat citations (+2 per paper, cap 5), recent activity (+1), and geography diversity (+1.5). Identity matching uses name + institution; namesakes at the same org are merged.
Cited by 5 papers
5 / 5 independent
Citations flagged as non-independent share the scholar's home institution (University of Oxford). EB-1A & O-1A petitions typically quote the independent-citation count as the stronger evidence of outside recognition.
· cites “QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning”
Wanqing Yang, Zeqiang Liew, Wei Yang Bryan Lim, Zehui Xiong + 4 more
IEEE Communications Surveys & Tutorials· cites “QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning”
Chao Yu, Akash Velu, Eugene Vinitsky, Jiaxuan Gao + 3 more
Advances in Neural Information Processing Systems (NeurIPS)· cites “QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning”
Long Yang, Yali Du, Guang Chen, Florian Walter + 3 more
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI)· cites “QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning”
· cites “QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning”
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