Konstantinos Bousmalis

4.2k total citations · 1 hit paper
16 papers, 1.5k citations indexed

About

Konstantinos Bousmalis is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Konstantinos Bousmalis has authored 16 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 4 papers in Control and Systems Engineering. Recurrent topics in Konstantinos Bousmalis's work include Bayesian Methods and Mixture Models (4 papers), Robot Manipulation and Learning (3 papers) and Reinforcement Learning in Robotics (3 papers). Konstantinos Bousmalis is often cited by papers focused on Bayesian Methods and Mixture Models (4 papers), Robot Manipulation and Learning (3 papers) and Reinforcement Learning in Robotics (3 papers). Konstantinos Bousmalis collaborates with scholars based in United Kingdom, United States and Netherlands. Konstantinos Bousmalis's co-authors include Dilip Krishnan, Dumitru Erhan, David Dohan, Nathan Silberman, Stefanos Zafeiriou, Björn W. Schuller, George Trigeorgis, Maja Pantić, Marc Méhu and Louis–Philippe Morency and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems and Image and Vision Computing.

In The Last Decade

Konstantinos Bousmalis

15 papers receiving 1.5k citations

Hit Papers

Unsupervised Pixel-Level Domain Adaptation with Generativ... 2017 2026 2020 2023 2017 250 500 750

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Konstantinos Bousmalis United Kingdom 9 1.0k 783 142 126 98 16 1.5k
Yuan Shi China 10 928 0.9× 1.3k 1.6× 99 0.7× 109 0.9× 129 1.3× 28 1.8k
Raghuraman Gopalan United States 9 1.1k 1.0× 1.1k 1.4× 104 0.7× 156 1.2× 88 0.9× 17 1.7k
Vineeth N Balasubramanian India 23 870 0.9× 818 1.0× 83 0.6× 92 0.7× 115 1.2× 103 1.7k
Minh-Thang Luong United States 12 1.1k 1.1× 1.8k 2.3× 158 1.1× 93 0.7× 126 1.3× 20 2.5k
Menglin Jia United States 5 570 0.6× 915 1.2× 167 1.2× 89 0.7× 72 0.7× 8 1.5k
Abdelmalik Taleb‐Ahmed France 18 776 0.8× 360 0.5× 87 0.6× 97 0.8× 322 3.3× 114 1.4k
Qizhe Xie United States 6 1.1k 1.1× 1.6k 2.0× 172 1.2× 113 0.9× 101 1.0× 12 2.2k
Jonathan Brandt United States 17 1.9k 1.8× 601 0.8× 103 0.7× 111 0.9× 342 3.5× 32 2.4k
Jingkang Yang China 11 1.4k 1.4× 1.4k 1.7× 170 1.2× 103 0.8× 112 1.1× 25 2.3k
Zuxuan Wu China 28 2.3k 2.3× 1.4k 1.8× 71 0.5× 158 1.3× 178 1.8× 82 3.0k

Countries citing papers authored by Konstantinos Bousmalis

Since Specialization
Citations

This map shows the geographic impact of Konstantinos Bousmalis'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 Konstantinos Bousmalis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Konstantinos Bousmalis more than expected).

Fields of papers citing papers by Konstantinos Bousmalis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Konstantinos Bousmalis. 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 Konstantinos Bousmalis. The network helps show where Konstantinos Bousmalis may publish in the future.

Co-authorship network of co-authors of Konstantinos Bousmalis

This figure shows the co-authorship network connecting the top 25 collaborators of Konstantinos Bousmalis. A scholar is included among the top collaborators of Konstantinos Bousmalis 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 Konstantinos Bousmalis. Konstantinos Bousmalis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Bauzá, Maria, Jingyi Chen, Valentin Dalibard, et al.. (2025). DemoStart: Demonstration-Led Auto-Curriculum Applied to Sim-to-Real with Multi-Fingered Robots. 6756–6763.
2.
Lee, Alex X., Coline Devin, Jost Tobias Springenberg, et al.. (2022). How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 4 indexed citations
3.
Brakel, Philémon, Steven Bohez, Leonard Hasenclever, Nicolas Heess, & Konstantinos Bousmalis. (2022). Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 10335–10342. 12 indexed citations
4.
Aytar, Yusuf, et al.. (2021). Manipulator-Independent Representations for Visual Imitation. 8 indexed citations
5.
Aytar, Yusuf, et al.. (2020). Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation. 2718–2724. 31 indexed citations
6.
Rao, Kanishka, et al.. (2019). Off-Policy Evaluation via Off-Policy Classification. arXiv (Cornell University). 32. 5437–5448. 2 indexed citations
7.
Bousmalis, Konstantinos, Nathan Silberman, David Dohan, Dumitru Erhan, & Dilip Krishnan. (2017). Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. 95–104. 942 indexed citations breakdown →
8.
Trigeorgis, George, Konstantinos Bousmalis, Stefanos Zafeiriou, & Björn W. Schuller. (2016). A Deep Matrix Factorization Method for Learning Attribute Representations. IEEE Transactions on Pattern Analysis and Machine Intelligence. 39(3). 417–429. 257 indexed citations
9.
Bousmalis, Konstantinos, Stefanos Zafeiriou, Louis–Philippe Morency, Maja Pantić, & Zoubin Ghahramani. (2015). Variational Infinite Hidden Conditional Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(9). 1917–1929. 4 indexed citations
10.
Trigeorgis, George, Konstantinos Bousmalis, Stefanos Zafeiriou, & Björn W. Schuller. (2014). A Deep Semi-NMF Model for Learning Hidden Representations. OPUS (Augsburg University). 1692–1700. 98 indexed citations
11.
Bousmalis, Konstantinos, Marc Méhu, & Maja Pantić. (2012). Towards the automatic detection of spontaneous agreement and disagreement based on nonverbal behaviour: A survey of related cues, databases, and tools. Image and Vision Computing. 31(2). 203–221. 57 indexed citations
12.
Bousmalis, Konstantinos, Stefanos Zafeiriou, Louis–Philippe Morency, & Maja Pantić. (2012). Infinite Hidden Conditional Random Fields for Human Behavior Analysis. IEEE Transactions on Neural Networks and Learning Systems. 24(1). 170–177. 26 indexed citations
13.
Bousmalis, Konstantinos, Louis–Philippe Morency, Stefanos Zafeiriou, & Maja Pantić. (2011). A Discriminative Nonparametric Bayesian Model: Infinite Hidden Conditional Random Fields. Neural Information Processing Systems. 3 indexed citations
14.
Bousmalis, Konstantinos, Louis–Philippe Morency, & Maja Pantić. (2011). Modeling hidden dynamics of multimodal cues for spontaneous agreement and disagreement recognition. University of Twente Research Information. 746–752. 37 indexed citations
15.
Bousmalis, Konstantinos, Marc Méhu, & Maja Pantić. (2009). Spotting agreement and disagreement: A survey of nonverbal audiovisual cues and tools. University of Twente Research Information. 1–9. 57 indexed citations
16.
Bousmalis, Konstantinos, et al.. (2005). A scouting-inspired evolutionary algorithm. 3. 1706–1712. 1 indexed citations

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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