Advanced Bandit Algorithms Research
Advanced Bandit Algorithms Research is a research topic within Management Science and Operations Research. Science Explorer counts 8.2k research works in it since 1956. 28.8% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the optimization of multi-armed bandit problems, including topics such as Bayesian optimization, contextual bandits, online learning, convex optimization, Thompson sampling, regret analysis, Gaussian process optimization, hyperparameter optimization, and adversarial multi-armed bandits.
- Bandit Optimization
- Bayesian Optimization
- Contextual Bandits
- Online Learning
- Convex Optimization
- Thompson Sampling
- Regret Analysis
- Gaussian Process Optimization
- Hyperparameter Optimization
- Adversarial Multi-Armed Bandits
- Research works
- 8.2k fractional, since 1956
- In the world top 10%
- 2.3k per year above
- Top-10% rate
- 28.8% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +72% the tick is no change
Which countries lead Advanced Bandit Algorithms Research research?
By volume, China and the United States publish the most (668 and 602 works in 2022–2025).
By volume, 2022–2025
- 1 China 668 works
- 2 United States 602 works
- 3 India 127 works
- 4 United Kingdom 83 works
- 5 France 71 works
- 6 Germany 64 works
- 7 Canada 58 works
- 8 Japan 55 works
- 9 Italy 54 works
- 10 Australia 46 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Advanced Bandit Algorithms Research research?
By volume in 2022–2025, Tsinghua University publishes the most Advanced Bandit Algorithms Research research, followed by Shanghai Jiao Tong University and University of Science and Technology of China.
By volume, 2022–2025
- 1 Tsinghua UniversityChina 30 works
- 2 Shanghai Jiao Tong UniversityChina 24 works
- 3 University of Science and Technology of ChinaChina 18 works
- 4 Cornell UniversityUnited States 17 works
- 5 Columbia UniversityUnited States 17 works
- 6 Google (United States)United States 16 works
- 7 University of Illinois Urbana-ChampaignUnited States 16 works
- 8 Stanford UniversityUnited States 15 works
- 9 Massachusetts Institute of TechnologyUnited States 15 works
- 10 Carnegie Mellon UniversityUnited States 15 works
Who are the leading researchers in Advanced Bandit Algorithms Research?
The most-cited researchers publishing on Advanced Bandit Algorithms Research include Philip S. Yu, David Silver and Michael I. Jordan.
- 1 Philip S. Yu United States 6.6k citations
- 2 David Silver United Kingdom 6.1k citations
- 3 Michael I. Jordan United States 4.8k citations
- 4 Zhu Han United States 4.7k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Advanced Bandit Algorithms Research research done?
The largest centres of Advanced Bandit Algorithms Research research in 2022–2025 are Beijing (China), Shanghai (China), Shenzhen (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Shenzhen.
Largest cities, 2022–2025
Where it is the local speciality
- ShenzhenCN · 53.4 works7.0×
Location quotient: how much more of its research is in Advanced Bandit Algorithms Research than the world average.
Where is the best place to study Advanced Bandit Algorithms Research?
Among universities, judged by research, Renmin University of China, Tsinghua University and University of Science and Technology of China score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Renmin University of ChinaChina | 70.1 | 57.6% | 17.1× | 13 | — |
| 2 | Tsinghua UniversityChina | 66.8 | 32.9% | 6.9× | 30 | +280.0% |
| 3 | University of Science and Technology of ChinaChina | 65.3 | 45.5% | 6.6× | 18 | +345.2% |
| 4 | Nanyang Technological UniversitySingapore | 58.3 | 53.9% | 6.0× | 11 | +87.3% |
| 5 | Carnegie Mellon UniversityUnited States | 54.9 | 18.7% | 16.6× | 15 | +143.1% |
| 6 | Massachusetts Institute of TechnologyUnited States | 50.8 | 18.1% | 11.1× | 15 | +76.9% |
| 7 | Shanghai Jiao Tong UniversityChina | 49.9 | 32.3% | 5.0× | 24 | +113.2% |
| 8 | Hong Kong University of Science and TechnologyHong Kong | 49.5 | 19.8% | 11.1× | 10 | +70.8% |
| 9 | City University of Hong KongHong Kong | 49.4 | 30.6% | 7.5× | 10 | — |
| 10 | National University of SingaporeSingapore | 49.3 | 25.4% | 5.5× | 12 | +249.6% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Advanced Bandit Algorithms Research research growing?
Output in 2018–2022 was 72% higher than in 2013–2017, peaking in 2023. The fastest-growing topics are Advanced Bandit Algorithms Research.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.