Tomer Koren

29 papers receiving 277 citations

Peers

Tomer Koren
Comparison fields: 5 of 47
  • Artificial Intelligence 202
  • Management Science and Operations Research 167
  • Computer Networks and Communications 60
  • Computational Mechanics 42
  • Computer Vision and Pattern Recognition 37
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Vianney Perchet France
Aaron Sidford United States
Ankan Saha United States
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Yuval Filmus Israel
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Countries citing papers authored by Tomer Koren

Since Specialization
Citations

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

Fields of papers citing papers by Tomer Koren

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tomer Koren

This figure shows the co-authorship network connecting the top 25 collaborators of Tomer Koren. A scholar is included among the top collaborators of Tomer Koren 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 Tomer Koren. Tomer Koren 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
Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry
3
2
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret
1
3
Adversarial Dueling Bandits
1
4
Learning Linear-Quadratic Regulators Efficiently with only √ T Regret
12
5
Revisiting the Generalization of Adaptive Gradient Methods
2
6
Shampoo: Preconditioned Stochastic Tensor Optimization
3
7
Affine-Invariant Online Optimization and the Low-rank Experts Problem
1
8 1
9
Online learning with feedback graphs without the graphs
6
10
The Limits of Learning with Missing Data
5
11
Online pricing with strategic and patient buyers
4
12
Fast rates for exp-concave empirical risk minimization
7
13
Bandit smooth convex optimization: improving the bias-variance tradeoff
5
14 29
15 2
16
Open Problem: Fast Stochastic Exp-Concave Optimization
3
17
Almost Optimal Exploration in Multi-Armed Bandits
102
18 3
19 12
20
Beating SGD: Learning SVMs in Sublinear Time
22

About Tomer Koren

Tomer Koren is a scholar working on Management Science and Operations Research, Computational Mathematics and Artificial Intelligence, having authored 30 papers that have together received 309 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (20 papers), Machine Learning and Algorithms (13 papers) and Optimization and Search Problems (8 papers). The work is most often cited by research in Management Science and Operations Research (167 citations), Artificial Intelligence (202 citations) and Numerical Analysis (24 citations). Tomer Koren has collaborated with scholars based in Israel, United States and United Kingdom. Frequent co-authors include Elad Hazan, Oren Somekh, Zohar Karnin, Ofer Dekel, Nati Srebro, Aharon Ben‐Tal, Yishay Mansour, Yuval Peres, Jian Ding and Alon Cohen. Their work appears in journals such as Operations Research, Mathematical Programming and SIAM Journal on Computing.

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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