Nicholas Roy

36 papers receiving 1.3k citations

Hit Papers

Toward Optimal Active Learning through Sampling Estimatio...20012026200920172001100200300400500

Peers

Nicholas Roy
Comparison fields: 5 of 89
  • Artificial Intelligence 834
  • Computer Vision and Pattern Recognition 506
  • Aerospace Engineering 357
  • Control and Systems Engineering 187
  • Computer Networks and Communications 142
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Stéphane Ross United States
Hanna Kurniawati Australia
Xudong Zhang China
Haobin Shi China
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Nicholas Roy relative to Stéphane Ross United States Stéphane Ross's profile →
Citations per field
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Citations per year

Countries citing papers authored by Nicholas Roy

Since Specialization
Citations

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

Fields of papers citing papers by Nicholas Roy

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nicholas Roy

This figure shows the co-authorship network connecting the top 25 collaborators of Nicholas Roy. A scholar is included among the top collaborators of Nicholas Roy 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 Nicholas Roy. Nicholas Roy 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
#WorkIndexed citations
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4 23
5 1
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Task-Conditioned Variational Autoencoders for Learning Movement Primitives
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10
Inferring Task Goals and Constraints using Bayesian Nonparametric Inverse Reinforcement Learning
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11
Learning over Subgoals for Efficient Navigation of Structured, Unknown Environments.
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12 13
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Bayesian Nonparametric Methods for Partially-Observable Reinforcement Learning
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Nonparametric Bayesian inference on multivariate exponential families
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15 26
16 2
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Nonparametric Bayesian Policy Priors for Reinforcement Learning
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18 77
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Continuous-State POMDPs with Hybrid Dynamics
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Toward Optimal Active Learning through Sampling Estimation of Error Reductionbreakdown →
566

About Nicholas Roy

Nicholas Roy is a scholar working on General Energy, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 36 papers that have together received 1.4k indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (10 papers), Robotic Path Planning Algorithms (8 papers) and Reinforcement Learning in Robotics (8 papers). The work is most often cited by research in Artificial Intelligence (834 citations), Computer Vision and Pattern Recognition (506 citations) and Aerospace Engineering (357 citations). Nicholas Roy has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Andrew McCallum, Thomas Kollar, Sebastian Thrun, Geoffrey J. Gordon, Emma Brunskill, Abraham Bachrach, Adam Bry, Charles Richter, Finale Doshi‐Velez and Kyel Ok. Their work appears in journals such as Advanced Materials, IEEE Transactions on Pattern Analysis and Machine Intelligence and The International Journal of Robotics Research.

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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