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.
Learning monocular reactive UAV control in cluttered natural environments
2013254 citationsStéphane Ross, Kumar Shaurya Shankar 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 Stéphane Ross'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 Stéphane Ross with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stéphane Ross more than expected).
This network shows the impact of papers produced by Stéphane Ross. 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 Stéphane Ross. The network helps show where Stéphane Ross may publish in the future.
Co-authorship network of co-authors of Stéphane Ross
This figure shows the co-authorship network connecting the top 25 collaborators of Stéphane Ross.
A scholar is included among the top collaborators of Stéphane Ross 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 Stéphane Ross. Stéphane Ross is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chang, Kai-Wei, He He, Stéphane Ross, Hal Daumé, & John Langford. (2016). A Credit Assignment Compiler for Joint Prediction. Neural Information Processing Systems. 29. 1705–1713.5 indexed citations
3.
Ross, Stéphane, Kumar Shaurya Shankar, Andreas Wendel, et al.. (2013). Learning monocular reactive UAV control in cluttered natural environments. 1765–1772.254 indexed citations breakdown →
Ross, Stéphane & Drew Bagnell. (2012). Agnostic System Identification for Model-Based Reinforcement Learning. International Conference on Machine Learning. 1905–1912.13 indexed citations
6.
Ross, Stéphane, et al.. (2011). A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes. Journal of Machine Learning Research. 12(48). 1729–1770.55 indexed citations
Ross, Stéphane, Geoffrey J. Gordon, & J. Andrew Bagnell. (2010). No-Regret Reductions for Imitation Learning and Structured Prediction. arXiv (Cornell University).43 indexed citations
Pineau, Joëlle, Stéphane Ross, & Brahim Chaib-draa. (2008). Bayes-Adaptive POMDPs: A New Perspective on the Explore-Exploit Tradeoff in Partially Observable Domains..1 indexed citations
Ross, Stéphane & Brahim Chaib-draa. (2007). AEMS: an anytime online search algorithm for approximate policy refinement in large POMDPs. International Joint Conference on Artificial Intelligence. 2592–2598.42 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.