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.
Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
2021320 citationsGabriel Dulac-Arnold, Nir Levine et al.Machine Learningprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Daniel J. Mankowitz
Since
Specialization
Citations
This map shows the geographic impact of Daniel J. Mankowitz'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 Daniel J. Mankowitz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel J. Mankowitz more than expected).
Fields of papers citing papers by Daniel J. Mankowitz
This network shows the impact of papers produced by Daniel J. Mankowitz. 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 Daniel J. Mankowitz. The network helps show where Daniel J. Mankowitz may publish in the future.
Co-authorship network of co-authors of Daniel J. Mankowitz
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel J. Mankowitz.
A scholar is included among the top collaborators of Daniel J. Mankowitz 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 Daniel J. Mankowitz. Daniel J. Mankowitz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Huang, Sandy H., Abbas Abdolmaleki, Philémon Brakel, et al.. (2021). A Constrained Multi-Objective Reinforcement Learning Framework.2 indexed citations
3.
Dulac-Arnold, Gabriel, Nir Levine, Daniel J. Mankowitz, et al.. (2021). Challenges of real-world reinforcement learning: definitions, benchmarks and analysis. Machine Learning. 110(9). 2419–2468.320 indexed citations breakdown →
4.
Gülçehre, Çaǧlar, Ziyu Wang, Alexander Novikov, et al.. (2020). RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning.. Neural Information Processing Systems.2 indexed citations
5.
Mankowitz, Daniel J., Nir Levine, Abbas Abdolmaleki, et al.. (2020). Robust Reinforcement Learning for Continuous Control with Model Misspecification. arXiv (Cornell University).1 indexed citations
Mankowitz, Daniel J., et al.. (2019). A Bayesian Approach to Robust Reinforcement Learning. Uncertainty in Artificial Intelligence. 648–658.4 indexed citations
Tessler, Chen, Daniel J. Mankowitz, & Shie Mannor. (2018). Reward Constrained Policy Optimization. arXiv (Cornell University).11 indexed citations
11.
Zahavy, Tom, et al.. (2018). Learning How Not to Act in Text-based Games. International Conference on Learning Representations.5 indexed citations
12.
Zahavy, Tom, et al.. (2018). Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning. arXiv (Cornell University). 31. 3562–3573.21 indexed citations
13.
Mankowitz, Daniel J., Timothy Mann, Pierre‐Luc Bacon, Doina Precup, & Shie Mannor. (2018). Learning Robust Options. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1).7 indexed citations
14.
Levine, Nir, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, & Shie Mannor. (2017). Shallow Updates for Deep Reinforcement Learning. Neural Information Processing Systems. 30. 3135–3145.5 indexed citations
Mann, Timothy, Daniel J. Mankowitz, & Shie Mannor. (2015). Learning When to Switch between Skills in a High Dimensional Domain. National Conference on Artificial Intelligence.1 indexed citations
18.
Mankowitz, Daniel J., Timothy Mann, & Shie Mannor. (2014). Time-regularized interrupting options. International Conference on Machine Learning.7 indexed citations
19.
Mann, Timothy, Daniel J. Mankowitz, & Shie Mannor. (2014). Time-Regularized Interrupting Options (TRIO). International Conference on Machine Learning. 1350–1358.2 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.