Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Deep Q-learning From Demonstrations
2018485 citationsTodd Hester, Olivier Pietquin et al.profile →
AudioLM: A Language Modeling Approach to Audio Generation
2023201 citationsZalán Borsos, Raphaël Marinier et al.IEEE/ACM Transactions on Audio Speech and Language Processingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Olivier Pietquin
Since
Specialization
Citations
This map shows the geographic impact of Olivier Pietquin'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 Olivier Pietquin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Olivier Pietquin more than expected).
Fields of papers citing papers by Olivier Pietquin
This network shows the impact of papers produced by Olivier Pietquin. 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 Olivier Pietquin. The network helps show where Olivier Pietquin may publish in the future.
Co-authorship network of co-authors of Olivier Pietquin
This figure shows the co-authorship network connecting the top 25 collaborators of Olivier Pietquin.
A scholar is included among the top collaborators of Olivier Pietquin 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 Olivier Pietquin. Olivier Pietquin is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Borsos, Zalán, Raphaël Marinier, Damien Vincent, et al.. (2023). AudioLM: A Language Modeling Approach to Audio Generation. IEEE/ACM Transactions on Audio Speech and Language Processing. 31. 2523–2533.201 indexed citations breakdown →
5.
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
6.
Geist, Matthieu, et al.. (2019). Learning from a Learner. International Conference on Machine Learning. 2990–2999.1 indexed citations
7.
Hussenot, Léonard, Matthieu Geist, & Olivier Pietquin. (2019). Targeted Attacks on Deep Reinforcement Learning Agents through Adversarial Observations.. arXiv (Cornell University).5 indexed citations
8.
Scherrer, Bruno, et al.. (2019). A Theory of Regularized Markov Decision Processes. HAL (Le Centre pour la Communication Scientifique Directe).6 indexed citations
9.
Élie, Romuald, Julien Pérolat, Mathieu Laurière, Matthieu Geist, & Olivier Pietquin. (2019). Approximate Fictitious Play for Mean Field Games. arXiv (Cornell University).5 indexed citations
10.
Fortunato, Meire, Mohammad Gheshlaghi Azar, Bilal Piot, et al.. (2018). Noisy Networks For Exploration. arXiv (Cornell University).115 indexed citations
11.
Hester, Todd, Olivier Pietquin, Marc Lanctot, et al.. (2017). Learning from Demonstrations for Real World Reinforcement Learning. arXiv (Cornell University).43 indexed citations
Pietquin, Olivier, et al.. (2015). Optimism in Active Learning. Computational Intelligence and Neuroscience. 2015. 1–17.3 indexed citations
15.
Pietquin, Olivier, et al.. (2014). Subspace Identification for Predictive State Representation by Nuclear Norm Minimization. HAL (Le Centre pour la Communication Scientifique Directe).1 indexed citations
16.
Pietquin, Olivier, et al.. (2011). Sample efficient on-line learning of optimal dialogue policies with kalman temporal differences. SPIRE - Sciences Po Institutional REpository.2 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.