This map shows the geographic impact of Timothy Mann'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 Timothy Mann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timothy Mann more than expected).
This network shows the impact of papers produced by Timothy Mann. 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 Timothy Mann. The network helps show where Timothy Mann may publish in the future.
Co-authorship network of co-authors of Timothy Mann
This figure shows the co-authorship network connecting the top 25 collaborators of Timothy Mann.
A scholar is included among the top collaborators of Timothy Mann 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 Timothy Mann. Timothy Mann 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.
Gowal, Sven, Po-Sen Huang, Aäron van den Oord, Timothy Mann, & Pushmeet Kohli. (2021). Self-supervised Adversarial Robustness for the Low-label, High-data Regime.6 indexed citations
2.
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
Riquelme, Carlos, Hugo Penedones, Damien Vincent, et al.. (2019). Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates. arXiv (Cornell University). 32. 11872–11882.3 indexed citations
5.
Mankowitz, Daniel J., et al.. (2019). A Bayesian Approach to Robust Reinforcement Learning. Uncertainty in Artificial Intelligence. 648–658.4 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
10.
Mann, Timothy, et al.. (2015). Off-policy Model-based Learning under Unknown Factored Dynamics. International Conference on Machine Learning. 711–719.6 indexed citations
11.
Mann, Timothy & Shie Mannor. (2014). Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations. International Conference on Machine Learning. 127–135.20 indexed citations
12.
Maillard, Odalric-Ambrym, Timothy Mann, & Shie Mannor. (2014). How hard is my MDP?" The distribution-norm to the rescue". Neural Information Processing Systems. 27. 1835–1843.11 indexed citations
13.
Mankowitz, Daniel J., Timothy Mann, & Shie Mannor. (2014). Time-regularized interrupting options. International Conference on Machine Learning.7 indexed citations
14.
Mann, Timothy, Daniel J. Mankowitz, & Shie Mannor. (2014). Time-Regularized Interrupting Options (TRIO). International Conference on Machine Learning. 1350–1358.2 indexed citations
15.
Mann, Timothy & Yoonsuck Choe. (2012). Directed Exploration in Reinforcement Learning with Transferred Knowledge. 59–76.14 indexed citations
Lamport, Leslie & Timothy Mann. (1997). Marching to Many Distant Drummers.1 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.