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.
Distributional Reinforcement Learning With Quantile Regression
2018294 citationsWill Dabney, Mark Rowland et al.profile →
A distributional code for value in dopamine-based reinforcement learning
2020236 citationsWill Dabney, Demis Hassabis et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Rémi Munos'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 Rémi Munos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rémi Munos more than expected).
This network shows the impact of papers produced by Rémi Munos. 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 Rémi Munos. The network helps show where Rémi Munos may publish in the future.
Co-authorship network of co-authors of Rémi Munos
This figure shows the co-authorship network connecting the top 25 collaborators of Rémi Munos.
A scholar is included among the top collaborators of Rémi Munos 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 Rémi Munos. Rémi Munos 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.
Guo, Zhaohan Daniel, Bernardo Ávila Pires, Mohammad Gheshlaghi Azar, et al.. (2020). Bootstrap Latent-Predictive Representations for Multitask Reinforcement Learning. International Conference on Machine Learning. 1. 3875–3886.4 indexed citations
2.
Rowland, Mark, Marc G. Bellemare, Will Dabney, Rémi Munos, & Yee Whye Teh. (2018). An Analysis of Categorical Distributional Reinforcement Learning. International Conference on Artificial Intelligence and Statistics. 29–37.4 indexed citations
3.
Kapturowski, Steven, Georg Ostrovski, John Quan, Rémi Munos, & Will Dabney. (2018). Recurrent Experience Replay in Distributed Reinforcement Learning.. International Conference on Learning Representations.94 indexed citations
4.
Fortunato, Meire, Mohammad Gheshlaghi Azar, Bilal Piot, et al.. (2018). Noisy Networks For Exploration. arXiv (Cornell University).115 indexed citations
5.
O’Donoghue, Brendan, Ian Osband, Rémi Munos, & Volodymyr Mnih. (2018). The Uncertainty Bellman Equation and Exploration.. International Conference on Machine Learning. 3839–3848.13 indexed citations
6.
Abdolmaleki, Abbas, Jost Tobias Springenberg, Yuval Tassa, et al.. (2018). Maximum a Posteriori Policy Optimisation. arXiv (Cornell University).10 indexed citations
7.
Ostrovski, Georg, Marc G. Bellemare, Aäron van den Oord, & Rémi Munos. (2017). Count-based exploration with neural density models. International Conference on Machine Learning. 2721–2730.43 indexed citations
8.
O’Donoghue, Brendan, Rémi Munos, Koray Kavukcuoglu, & Volodymyr Mnih. (2017). Combining policy gradient and Q-learning. International Conference on Learning Representations.9 indexed citations
9.
Ghavamzadeh, Mohammad, Hilbert J. Kappen, Mohammad Gheshlaghi Azar, & Rémi Munos. (2011). Speedy Q-Learning. Neural Information Processing Systems. 24. 2411–2419.43 indexed citations
Lazaric, Alessandro, Mohammad Ghavamzadeh, & Rémi Munos. (2010). Finite-Sample Analysis of LSTD. HAL (Le Centre pour la Communication Scientifique Directe). 615–622.23 indexed citations
12.
Lazaric, Alessandro, Mohammad Ghavamzadeh, & Rémi Munos. (2010). Analysis of a Classification-based Policy Iteration Algorithm. HAL (Le Centre pour la Communication Scientifique Directe). 607–614.18 indexed citations
13.
Munos, Rémi, et al.. (2009). Compressed Least-Squares Regression. HAL (Le Centre pour la Communication Scientifique Directe). 22. 1213–1221.40 indexed citations
14.
Bubeck, Sébastien, Rémi Munos, Gilles Stoltz, & Csaba Szepesvári. (2008). Online Optimization in X-Armed Bandits. RePEc: Research Papers in Economics. 21. 201–208.68 indexed citations
15.
Munos, Rémi & Csaba Szepesvári. (2008). Finite-Time Bounds for Fitted Value Iteration. Journal of Machine Learning Research. 9(27). 815–857.111 indexed citations
16.
Antos, András, Csaba Szepesvári, & Rémi Munos. (2007). Fitted Q-iteration in continuous action-space MDPs. HAL (Le Centre pour la Communication Scientifique Directe). 20. 9–16.69 indexed citations
17.
Munos, Rémi. (2005). Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation. Journal of Machine Learning Research. 7(14). 1012–1017.1 indexed citations
18.
Munos, Rémi. (2005). Error bounds for approximate value iteration. National Conference on Artificial Intelligence. 1006–1011.22 indexed citations
19.
Munos, Rémi & Andrew W. Moore. (2000). Rates of Convergence for Variable Resolution Schemes in Optimal Control. International Conference on Machine Learning. 647–654.15 indexed citations
20.
Munos, Rémi & Andrew Moore. (1998). Barycentric Interpolators for Continuous Space and Time Reinforcement Learning. Neural Information Processing Systems. 11. 1024–1030.21 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.