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.
STOMP: Stochastic trajectory optimization for motion planning
2011568 citationsEvangelos A. Theodorou et al.profile →
Information theoretic MPC for model-based reinforcement learning
2017262 citationsGrady Williams, Brian Goldfain et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Evangelos A. Theodorou
Since
Specialization
Citations
This map shows the geographic impact of Evangelos A. Theodorou'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 Evangelos A. Theodorou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Evangelos A. Theodorou more than expected).
Fields of papers citing papers by Evangelos A. Theodorou
This network shows the impact of papers produced by Evangelos A. Theodorou. 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 Evangelos A. Theodorou. The network helps show where Evangelos A. Theodorou may publish in the future.
Co-authorship network of co-authors of Evangelos A. Theodorou
This figure shows the co-authorship network connecting the top 25 collaborators of Evangelos A. Theodorou.
A scholar is included among the top collaborators of Evangelos A. Theodorou 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 Evangelos A. Theodorou. Evangelos A. Theodorou is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chen, Tianrong, et al.. (2020). Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory.. 728–738.3 indexed citations
9.
Zhao, Pan, et al.. (2020). L1-GP: L1 Adaptive Control with Bayesian Learning. 826–837.9 indexed citations
Drews, Paul, Grady Williams, Brian Goldfain, Evangelos A. Theodorou, & James M. Rehg. (2017). Aggressive Deep Driving: Combining Convolutional Neural Networks and Model Predictive Control. 133–142.19 indexed citations
14.
Pan, Yunpeng, Ching-An Cheng, Kamil Saigol, et al.. (2017). Imitation Learning for Agile Autonomous Driving. arXiv (Cornell University).1 indexed citations
15.
Pan, Yunpeng, Xinyan Yan, Evangelos A. Theodorou, & Byron Boots. (2017). Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control.. International Conference on Machine Learning. 2760–2768.11 indexed citations
16.
Pan, Yunpeng & Evangelos A. Theodorou. (2014). Probabilistic Differential Dynamic Programming. Neural Information Processing Systems. 27. 1907–1915.42 indexed citations
17.
Meier, Franziska, Evangelos A. Theodorou, & Stefan Schaal. (2012). Movement Segmentation and Recognition for Imitation Learning. MPG.PuRe (Max Planck Society). 761–769.19 indexed citations
Theodorou, Evangelos A., Jonas Buchli, & Stefan Schaal. (2010). A Generalized Path Integral Control Approach to Reinforcement Learning. Journal of Machine Learning Research. 11(104). 3137–3181.290 indexed citations
20.
Theodorou, Evangelos A., Jonas Buchli, & Stefan Schaal. (2010). Learning Policy Improvements with Path Integrals. International Conference on Artificial Intelligence and Statistics. 828–835.33 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.