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.
This map shows the geographic impact of Shie Mannor'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 Shie Mannor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shie Mannor more than expected).
This network shows the impact of papers produced by Shie Mannor. 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 Shie Mannor. The network helps show where Shie Mannor may publish in the future.
Co-authorship network of co-authors of Shie Mannor
This figure shows the co-authorship network connecting the top 25 collaborators of Shie Mannor.
A scholar is included among the top collaborators of Shie Mannor 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 Shie Mannor. Shie Mannor is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tessler, Chen, et al.. (2019). Stabilizing Off-Policy Reinforcement Learning with Conservative Policy Gradients. arXiv (Cornell University).1 indexed citations
3.
Ghavamzadeh, Mohammad, et al.. (2019). Multi-Step Greedy and Approximate Real Time Dynamic Programming. arXiv (Cornell University).2 indexed citations
4.
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
5.
Mannor, Shie, et al.. (2017). Proxy Voting for Better Outcomes. Adaptive Agents and Multi-Agents Systems. 858–866.6 indexed citations
6.
Baram, Nir, et al.. (2017). End-to-End Differentiable Adversarial Imitation Learning. International Conference on Machine Learning. 390–399.30 indexed citations
7.
Busa‐Fekete, Róbert, Balázs Szörényi, Paul Weng, & Shie Mannor. (2017). Multi-objective Bandits: Optimizing the Generalized Gini Index. International Conference on Machine Learning. 625–634.6 indexed citations
8.
Dalal, Gal, et al.. (2017). Concentration Bounds for Two Timescale Stochastic Approximation with Applications to Reinforcement Learning. arXiv (Cornell University).1 indexed citations
9.
Farahmand, Amir‐massoud, Mohammad Ghavamzadeh, Csaba Szepesvári, & Shie Mannor. (2016). Regularized policy iteration with nonparametric function spaces. Journal of Machine Learning Research. 17(1). 4809–4874.19 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
Dekel, Ofer, et al.. (2011). Bundle Selling by Online Estimation of Valuation Functions. International Conference on Machine Learning. 1137–1144.3 indexed citations
14.
Abernethy, Jacob & Shie Mannor. (2011). Does an Efficient Calibrated Forecasting Strategy Exist. Conference on Learning Theory. 809–812.2 indexed citations
15.
Johari, Ramesh, et al.. (2011). Committing Bandits. Neural Information Processing Systems. 24. 1557–1565.3 indexed citations
16.
Xu, Huan, Shie Mannor, & Constantine Caramanis. (2008). Robustness, Risk, and Regularization in Support Vector Machines. 7. 425–37.2 indexed citations
Mannor, Shie & Nahum Shimkin. (2004). A Geometric Approach to Multi-Criterion Reinforcement Learning. Journal of Machine Learning Research. 5. 325–360.62 indexed citations
19.
Even-Dar, Eyal, Shie Mannor, & Yishay Mansour. (2003). Action elimination and stopping conditions for reinforcement learning. International Conference on Machine Learning. 162–169.12 indexed citations
20.
Engel, Yaakov & Shie Mannor. (2001). Learning Embedded Maps of Markov Processes. International Conference on Machine Learning. 138–145.4 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.