Matthieu Geist

3.0k total citations
27 papers, 261 citations indexed

About

Matthieu Geist is a scholar working on Artificial Intelligence, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Matthieu Geist has authored 27 papers receiving a total of 261 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 4 papers in Control and Systems Engineering and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Matthieu Geist's work include Reinforcement Learning in Robotics (10 papers), Speech and dialogue systems (4 papers) and Anomaly Detection Techniques and Applications (4 papers). Matthieu Geist is often cited by papers focused on Reinforcement Learning in Robotics (10 papers), Speech and dialogue systems (4 papers) and Anomaly Detection Techniques and Applications (4 papers). Matthieu Geist collaborates with scholars based in France, United States and Germany. Matthieu Geist's co-authors include Olivier Pietquin, Bilal Piot, Léonard Hussenot, Miguel Olivares-Mendez, Holger Voos, Olivier Bachem, Cédric Pradalier, Nico F. Declercq, Piotr Stańczyk and Sertan Girgin and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Machine Learning and Applied Sciences.

In The Last Decade

Matthieu Geist

24 papers receiving 253 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthieu Geist France 10 176 52 29 25 23 27 261
Hongyao Tang China 7 113 0.6× 50 1.0× 29 1.0× 38 1.5× 34 1.5× 18 211
Scott Fujimoto Canada 3 114 0.6× 51 1.0× 36 1.2× 38 1.5× 16 0.7× 6 191
Steven Kapturowski United States 3 144 0.8× 30 0.6× 34 1.2× 22 0.9× 14 0.6× 5 194
Nibedan Panda India 9 164 0.9× 40 0.8× 31 1.1× 29 1.2× 20 0.9× 28 233
Chenjia Bai China 6 94 0.5× 41 0.8× 27 0.9× 25 1.0× 29 1.3× 26 173
Archana Sarangi India 9 118 0.7× 32 0.6× 43 1.5× 36 1.4× 22 1.0× 22 244
Zhaoyang Yang China 5 137 0.8× 54 1.0× 89 3.1× 30 1.2× 36 1.6× 8 254
Steffen Udluft Germany 11 251 1.4× 89 1.7× 26 0.9× 44 1.8× 16 0.7× 33 357
Alberto Maria Metelli Italy 7 109 0.6× 43 0.8× 22 0.8× 22 0.9× 18 0.8× 32 205
Parul Agarwal India 10 147 0.8× 26 0.5× 32 1.1× 25 1.0× 51 2.2× 23 268

Countries citing papers authored by Matthieu Geist

Since Specialization
Citations

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

Fields of papers citing papers by Matthieu Geist

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthieu Geist

This figure shows the co-authorship network connecting the top 25 collaborators of Matthieu Geist. A scholar is included among the top collaborators of Matthieu Geist 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 Matthieu Geist. Matthieu Geist 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
1.
Geist, Matthieu, et al.. (2024). DRIFT: Deep Reinforcement Learning for Intelligent Floating Platforms Trajectories. Open Repository and Bibliography (University of Luxembourg). 14034–14041. 3 indexed citations
2.
Ferret, Johan, Lior Shani, Roee Aharoni, et al.. (2023). Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback. 6252–6272. 10 indexed citations
3.
Heinze, Christoph, et al.. (2022). Laser Sensing System for Contactless Detection of Subsurface Defects in Concrete Tunnel Lining. e-Journal of Nondestructive Testing. 27(9). 1 indexed citations
4.
Declercq, Nico F., et al.. (2022). Learning the propagation properties of rectangular metal plates for Lamb wave-based mapping. Ultrasonics. 123. 106705–106705. 5 indexed citations
5.
Zeghidour, Neil, et al.. (2022). Combined Grid and Feature-based Mapping of Metal Structures with Ultrasonic Guided Waves. 2022 International Conference on Robotics and Automation (ICRA). 5056–5062. 2 indexed citations
6.
Dadashi, Robert, Nino Vieillard, Léonard Hussenot, et al.. (2022). Offline Reinforcement Learning as Anti-exploration. Proceedings of the AAAI Conference on Artificial Intelligence. 36(7). 8106–8114. 9 indexed citations
7.
Andrychowicz, Marcin, Anton Raichuk, Piotr Stańczyk, et al.. (2021). What Matters for On-Policy Deep Actor-Critic Methods? A Large-Scale Study. International Conference on Learning Representations. 26 indexed citations
8.
Geist, Matthieu, et al.. (2021). Evaluation of Prioritized Deep System Identification on a Path Following Task. Journal of Intelligent & Robotic Systems. 101(4).
9.
Geist, Matthieu, et al.. (2021). A FastSLAM Approach Integrating Beamforming Maps for Ultrasound-Based Robotic Inspection of Metal Structures. IEEE Robotics and Automation Letters. 6(2). 2908–2913. 11 indexed citations
10.
Merdivan, Erinç, Deepika Singh, Sten Hanke, et al.. (2020). Human Annotated Dialogues Dataset for Natural Conversational Agents. Applied Sciences. 10(3). 762–762. 13 indexed citations
11.
Hussenot, Léonard, Matthieu Geist, & Olivier Pietquin. (2019). Targeted Attacks on Deep Reinforcement Learning Agents through Adversarial Observations.. arXiv (Cornell University). 5 indexed citations
12.
Élie, Romuald, Julien Pérolat, Mathieu Laurière, Matthieu Geist, & Olivier Pietquin. (2019). Approximate Fictitious Play for Mean Field Games. arXiv (Cornell University). 5 indexed citations
13.
Geist, Matthieu, et al.. (2019). Learning from a Learner. International Conference on Machine Learning. 2990–2999. 1 indexed citations
14.
Geist, Matthieu, et al.. (2018). Stable and Efficient Policy Evaluation. IEEE Transactions on Neural Networks and Learning Systems. 30(6). 1831–1840. 8 indexed citations
15.
Piot, Bilal, Matthieu Geist, & Olivier Pietquin. (2016). Bridging the Gap Between Imitation Learning and Inverse Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems. 28(8). 1814–1826. 59 indexed citations
16.
Geist, Matthieu. (2015). Soft-max boosting. Machine Learning. 100(2-3). 305–332. 4 indexed citations
17.
Geist, Matthieu, et al.. (2013). Particle swarm optimisation of spoken dialogue system strategies. 470–474. 1 indexed citations
18.
Geist, Matthieu, et al.. (2012). A Comprehensive Reinforcement Learning Framework for Dialogue Management Optimization. IEEE Journal of Selected Topics in Signal Processing. 6(8). 891–902. 22 indexed citations
19.
Pietquin, Olivier, et al.. (2011). Sample-efficient batch reinforcement learning for dialogue management optimization. SPIRE - Sciences Po Institutional REpository. 7(3). 1–21. 33 indexed citations
20.
Gašić, Milica, et al.. (2011). Uncertainty management for on-line optimisation of a POMDP-based large-scale spoken dialogue system. 1301–1304. 10 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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