Eric Wiewiora

1.8k total citations · 1 hit paper
8 papers, 1.0k citations indexed

About

Eric Wiewiora is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Cognitive Neuroscience. According to data from OpenAlex, Eric Wiewiora has authored 8 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 1 paper in Computer Vision and Pattern Recognition and 1 paper in Cognitive Neuroscience. Recurrent topics in Eric Wiewiora's work include Reinforcement Learning in Robotics (5 papers), Machine Learning and Algorithms (3 papers) and Bayesian Modeling and Causal Inference (2 papers). Eric Wiewiora is often cited by papers focused on Reinforcement Learning in Robotics (5 papers), Machine Learning and Algorithms (3 papers) and Bayesian Modeling and Causal Inference (2 papers). Eric Wiewiora collaborates with scholars based in United States, India and Canada. Eric Wiewiora's co-authors include Carolina Galleguillos, Andrew Rabinovich, Andrea Vedaldi, Serge Belongie, Alexander L. Strehl, John Langford, Michael L. Littman, Lihong Li, David Silver and Shalabh Bhatnagar and has published in prestigious journals such as Journal of Vision and International Conference on Machine Learning.

In The Last Decade

Eric Wiewiora

7 papers receiving 942 citations

Hit Papers

align trajectories

What are hit papers?

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.

Objects in Context

2007 419 citations Andrew Rabinovich, Andrea Vedaldi et al. profile →

Peers

Countries citing papers authored by Eric Wiewiora

Since Specialization

Citations

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

Fields of papers citing papers by Eric Wiewiora

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eric Wiewiora

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

All Works

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8 of 8 papers shown

#	Work	Indexed citations
1	Learning Optimal Gaze Decomposition 2010 ·Journal of Vision ·Eric Wiewiora, David Van Den Berg, Jochen Triesch, Tomonori Hashiyama	0
2	Fast gradient-descent methods for temporal-difference learning with linear function approximation 2009 ·Richard S. Sutton, Hamid Reza Maei, Doina Precup, Shalabh Bhatnagar, David Silver, Csaba Szepesvári, Eric Wiewiora	258
3	Objects in Context breakdown → 2007 ·Andrew Rabinovich, Andrea Vedaldi, Carolina Galleguillos, Eric Wiewiora, Serge Belongie	419
4	Modeling probability distributions with predictive state representations 2007 ·Garrison W. Cottrell, Charles Elkan, Eric Wiewiora	12
5	PAC model-free reinforcement learning 2006 ·Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman	204
6	Learning predictive representations from a history 2005 ·Eric Wiewiora	22
7	Ecient Exploration for Reinforcement Learning 2004 ·Eric Wiewiora	3
8	Principled methods for advising reinforcement learning agents 2003 ·International Conference on Machine Learning ·Eric Wiewiora, Garrison W. Cottrell, Charles Elkan	84

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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