Devin Schwab

447 total citations
6 papers, 214 citations indexed

About

Devin Schwab is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Devin Schwab has authored 6 papers receiving a total of 214 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 1 paper in Control and Systems Engineering. Recurrent topics in Devin Schwab's work include Reinforcement Learning in Robotics (5 papers), Adversarial Robustness in Machine Learning (2 papers) and Data Stream Mining Techniques (2 papers). Devin Schwab is often cited by papers focused on Reinforcement Learning in Robotics (5 papers), Adversarial Robustness in Machine Learning (2 papers) and Data Stream Mining Techniques (2 papers). Devin Schwab collaborates with scholars based in United States, Switzerland and Italy. Devin Schwab's co-authors include Manuela Veloso, Soumya Ray, Roland Hafner, Abbas Abdolmaleki, Thomas Lampe, Francesco Nori, Martin Riedmiller, Michael Neunert and Jost Tobias Springenberg and has published in prestigious journals such as Machine Learning, arXiv (Cornell University) and Adaptive Agents and Multi-Agents Systems.

In The Last Decade

Devin Schwab

6 papers receiving 209 citations

Peers

Countries citing papers authored by Devin Schwab

Since Specialization

Citations

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

Fields of papers citing papers by Devin Schwab

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Devin Schwab

This figure shows the co-authorship network connecting the top 25 collaborators of Devin Schwab. A scholar is included among the top collaborators of Devin Schwab 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 Devin Schwab. Devin Schwab is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

#	Work	Indexed citations
1	Tensor Action Spaces for Multi-agent Robot Transfer Learning 2020 ·Devin Schwab, (unknown), Manuela Veloso	1
2	Learning Primitive Skills for Mobile Robots 2019 ·(unknown), Devin Schwab, Manuela Veloso	5
3	Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup 2019 ·arXiv (Cornell University) ·Devin Schwab, Jost Tobias Springenberg, (unknown), Thomas Lampe, Michael Neunert, Abbas Abdolmaleki, (unknown), Roland Hafner, Francesco Nori, Martin Riedmiller	2
4	Zero Shot Transfer Learning for Robot Soccer 2018 ·Adaptive Agents and Multi-Agents Systems ·Devin Schwab, (unknown), Manuela Veloso	7
5	Offline reinforcement learning with task hierarchies 2017 ·Machine Learning ·Devin Schwab, Soumya Ray	7
6	FOR LEAST-SQUARES POLICY ITERATION 2016 ·Devin Schwab	192

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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