Tomer Koren
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- Advanced Bandit Algorithms Research 20
- Auction Theory and Applications 5
- Artificial Intelligence top 5%
- Machine Learning and Algorithms 13
- Stochastic Gradient Optimization Techniques 7
- Reinforcement Learning in Robotics 3
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- Advanced Optimization Algorithms Research 3
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- Optimization and Search Problems 8
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- Sparse and Compressive Sensing Techniques 6
- Co-authors
- Elad HazanOren SomekhZohar KarninOfer DekelNati SrebroAharon Ben‐TalYishay MansourYuval Peres
- Partner nations
- IsraelUnited StatesUnited Kingdom
In The Last Decade
Tomer Koren
29 papers receiving 277 citations
Peers
Comparison fields: 5 of 47
- Management Science and Operations Research 167
- Artificial Intelligence 202
- Numerical Analysis 24
- Computational Mathematics 2
- Computer Networks and Communications 60
Countries citing papers authored by Tomer Koren
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
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
The 25 scholars most cited alongside Tomer Koren, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry | 2021 | 3 |
| 2 | Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret | 2021 | 1 |
| 3 | Adversarial Dueling Bandits | 2021 | 1 |
| 4 | Learning Linear-Quadratic Regulators Efficiently with only √ T Regret | 2019 | 12 |
| 5 | Revisiting the Generalization of Adaptive Gradient Methods | 2019 | 2 |
| 6 | Shampoo: Preconditioned Stochastic Tensor Optimization | 2018 | 3 |
| 7 | Affine-Invariant Online Optimization and the Low-rank Experts Problem | 2017 | 1 |
| 8 | 2017 | 1 | |
| 9 | Online learning with feedback graphs without the graphs | 2016 | 6 |
| 10 | The Limits of Learning with Missing Data | 2016 | 5 |
| 11 | Online pricing with strategic and patient buyers | 2016 | 4 |
| 12 | Fast rates for exp-concave empirical risk minimization | 2015 | 7 |
| 13 | Bandit smooth convex optimization: improving the bias-variance tradeoff | 2015 | 5 |
| 14 | 2015 | 29 | |
| 15 | 2014 | 2 | |
| 16 | Open Problem: Fast Stochastic Exp-Concave Optimization | 2013 | 3 |
| 17 | Almost Optimal Exploration in Multi-Armed Bandits | 2013 | 102 |
| 18 | 2012 | 3 | |
| 19 | 2012 | 12 | |
| 20 | Beating SGD: Learning SVMs in Sublinear Time | 2011 | 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), Optimization and Search Problems (8 papers), Stochastic Gradient Optimization Techniques (7 papers), Sparse and Compressive Sensing Techniques (6 papers), Auction Theory and Applications (5 papers), Advanced Optimization Algorithms Research (3 papers) and Reinforcement Learning in Robotics (3 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.
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.