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.
Maximum Entropy Inverse Reinforcement Learning
2018795 citationsBrian D. Ziebart, Andrew L. Maas 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 Brian D. Ziebart
Since
Specialization
Citations
This map shows the geographic impact of Brian D. Ziebart'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 Brian D. Ziebart with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian D. Ziebart more than expected).
Fields of papers citing papers by Brian D. Ziebart
This network shows the impact of papers produced by Brian D. Ziebart. 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 Brian D. Ziebart. The network helps show where Brian D. Ziebart may publish in the future.
Co-authorship network of co-authors of Brian D. Ziebart
This figure shows the co-authorship network connecting the top 25 collaborators of Brian D. Ziebart.
A scholar is included among the top collaborators of Brian D. Ziebart 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 Brian D. Ziebart. Brian D. Ziebart 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
1.
Ziebart, Brian D., et al.. (2021). Distributionally Robust Imitation Learning. Neural Information Processing Systems. 34.1 indexed citations
Eugenio, Barbara Di, et al.. (2020). Goal Summarization for Human-Human Health Coaching Dialogues. The Florida AI Research Society. 317–322.3 indexed citations
Liu, Anqi, et al.. (2019). Active Learning for Probabilistic Structured Prediction of Cuts and Matchings.. International Conference on Machine Learning. 563–572.1 indexed citations
7.
Rezaei, Ashkan, et al.. (2019). Fair Logistic Regression: An Adversarial Perspective.. arXiv (Cornell University).1 indexed citations
Petrik, Marek, et al.. (2018). Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes. Neural Information Processing Systems. 31. 8939–8949.2 indexed citations
10.
Zhang, Xinhua, et al.. (2018). Efficient and Consistent Adversarial Bipartite Matching. International Conference on Machine Learning. 1456–1465.3 indexed citations
11.
Ziebart, Brian D., et al.. (2017). Adversarial Surrogate Losses for Ordinal Regression. Neural Information Processing Systems. 30. 563–573.10 indexed citations
12.
Chen, Xiangli, Mathew Monfort, Anqi Liu, & Brian D. Ziebart. (2016). Robust Covariate Shift Regression. International Conference on Artificial Intelligence and Statistics. 1270–1279.13 indexed citations
13.
Liu, Anqi, et al.. (2016). Adversarial Multiclass Classification: A Risk Minimization Perspective. Neural Information Processing Systems. 29. 559–567.2 indexed citations
14.
Chen, Hao & Brian D. Ziebart. (2015). Predictive Inverse Optimal Control for Linear-Quadratic-Gaussian Systems. International Conference on Artificial Intelligence and Statistics. 165–173.1 indexed citations
15.
Wei, Xing, et al.. (2015). Adversarial cost-sensitive classification. Uncertainty in Artificial Intelligence. 92–101.6 indexed citations
16.
Li, Jia, et al.. (2015). Social information improves location prediction in the wild. National Conference on Artificial Intelligence. 25–32.8 indexed citations
17.
Wang, Hong, et al.. (2015). Adversarial prediction games for multivariate losses. Neural Information Processing Systems. 28. 2728–2736.12 indexed citations
18.
Liu, Anqi & Brian D. Ziebart. (2014). Robust Classification Under Sample Selection Bias. Neural Information Processing Systems. 27. 37–45.35 indexed citations
Ziebart, Brian D., Andrew L. Maas, J. Andrew Bagnell, & Anind K. Dey. (2009). Human Behavior Modeling with Maximum Entropy Inverse Optimal Control. National Conference on Artificial Intelligence. 92–97.31 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.