Yishay Mansour

226 papers receiving 8.3k citations

Hit Papers

Policy Gradient Methods for Reinforcement Learning with F...1999202620082017199950010001.5k2.0k2.5k

Peers

Yishay Mansour
Comparison fields: 5 of 166
  • Artificial Intelligence 5.0k
  • Computer Networks and Communications 2.3k
  • Management Science and Operations Research 2.0k
  • Computational Theory and Mathematics 1.7k
  • Electrical and Electronic Engineering 979
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Citations per field
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Citations per year

Countries citing papers authored by Yishay Mansour

Since Specialization
Citations

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

Fields of papers citing papers by Yishay Mansour

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yishay Mansour

This figure shows the co-authorship network connecting the top 25 collaborators of Yishay Mansour. A scholar is included among the top collaborators of Yishay Mansour 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 Yishay Mansour. Yishay Mansour 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
#WorkIndexed citations
1 0
2 1
3 0
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Top-$k$ Combinatorial Bandits with Full-Bandit Feedback
2
5
Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
4
6
Learning Decision Trees with Stochastic Linear Classifiers.
1
7
Robust Inference for Multiclass Classification.
1
8
Efficient Co-Training of Linear Separators under Weak Dependence
1
9
Learning Multiple Tasks using Shared Hypotheses
20
10 8
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Reliable Agnostic Learning.
8
12
Online Learning with Global Cost Functions
12
13 42
14
Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems
148
15
Action elimination and stopping conditions for reinforcement learning
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Nash convergence of gradient dynamics in general-sum games
152
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A sparse sampling algorithm for near-optimal planning in large Markov decision processes
129
18
Boosting with Multi-Way Branching in Decision Trees
3
19
Learning Decision Trees Using the Fourier Sprectrum (Extended Abstract)
1
20 1

About Yishay Mansour

Yishay Mansour is a scholar working on Management Science and Operations Research, Artificial Intelligence and Computer Networks and Communications, having authored 232 papers that have together received 9.0k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (63 papers), Optimization and Search Problems (47 papers) and Advanced Bandit Algorithms Research (46 papers). The work is most often cited by research in Management Science and Operations Research (2.0k citations), Artificial Intelligence (5.0k citations) and Computational Theory and Mathematics (1.7k citations). Yishay Mansour has collaborated with scholars based in Israel, United States and India. Frequent co-authors include David McAllester, Satinder Singh, Richard S. Sutton, Michael Kearns, Eyal Kushilevitz, Noam Nisan, Andrew Y. Ng, Nathan Linial, Eyal Even-Dar and Mehryar Mohri. Their work appears in journals such as Nature Communications, Bioinformatics and Journal of Political Economy.

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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