Branislav Kveton

65 papers receiving 641 citations

Peers

Branislav Kveton
Comparison fields: 5 of 57
  • Artificial Intelligence 428
  • Management Science and Operations Research 273
  • Computer Networks and Communications 215
  • Information Systems 183
  • Electrical and Electronic Engineering 95
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Countries citing papers authored by Branislav Kveton

Since Specialization
Citations

This map shows the geographic impact of Branislav Kveton's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Branislav Kveton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Branislav Kveton more than expected).

Fields of papers citing papers by Branislav Kveton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Branislav Kveton. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Branislav Kveton. The network helps show where Branislav Kveton may publish in the future.

Co-authorship network of co-authors of Branislav Kveton

This figure shows the co-authorship network connecting the top 25 collaborators of Branislav Kveton. A scholar is included among the top collaborators of Branislav Kveton based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Branislav Kveton. Branislav Kveton is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
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2 5
3 1
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6
Graphical Models Meet Bandits: A Variational Thompson Sampling Approach.
2
7
Differentiable Meta-Learning in Contextual Bandits.
1
8
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
1
9
BubbleRank: Safe Online Learning to Rerank.
1
10
Influence Maximization with Semi-Bandit Feedback.
3
11
DCM bandits: learning to rank with multiple clicks
2
12
Combinatorial cascading bandits
24
13
Optimal greedy diversity for recommendation
37
14
Diversified Utility Maximization for Recommendations.
8
15
SPPM: sparse privacy preserving mappings
3
16
Minimal Interaction Search: Multi-Way Search with Item Categories
5
17
Automatic identity inference for smart TVs
1
18
Online learning with expert advice and finite-horizon constraints
4
19
Adaptive timeout policies for fast fine-grained power management
12
20
Learning basis functions in hybrid domains
5

About Branislav Kveton

Branislav Kveton is a scholar working on Management Science and Operations Research, Artificial Intelligence and Computer Networks and Communications, having authored 68 papers that have together received 672 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (34 papers), Machine Learning and Algorithms (18 papers) and Optimization and Search Problems (17 papers). The work is most often cited by research in Management Science and Operations Research (273 citations), Artificial Intelligence (428 citations) and Computer Networks and Communications (215 citations). Branislav Kveton has collaborated with scholars based in United States, Germany and Canada. Frequent co-authors include Azin Ashkan, Zheng Wen, Miloš Hauskrecht, Csaba Szepesvári, Shlomo Berkovsky, Wen Zheng, Hung Bui, Nadia Fawaz, Jaya Kawale and Brian Eriksson. Their work appears in journals such as IEEE Journal of Selected Topics in Signal Processing, IEEE Software and Computer Communications.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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