Tor Lattimore
Impact in
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- Advanced Bandit Algorithms Research
- Auction Theory and Applications
- Artificial Intelligence top 5%
- Reinforcement Learning in Robotics
- Machine Learning and Algorithms
- Data Stream Mining Techniques
Papers in
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- Advanced Bandit Algorithms Research 39
- Auction Theory and Applications 7
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- Machine Learning and Algorithms 22
- Reinforcement Learning in Robotics 12
- Algorithms and Data Compression 4
- Stochastic Gradient Optimization Techniques 3
- Co-authors
- Csaba Szepesvári (23 shared papers)Marcus Hütter (14 shared papers)Rémi Munos (1 shared paper)András György (5 shared papers)Christoph Dann (1 shared paper)Emma Brunskill (1 shared paper)Koby Crammer (2 shared papers)Ruitong Huang (2 shared papers)
- Journals
- Theoretical Computer Science (3 papers)Journal of Machine Learning Research (2 papers)Lecture notes in computer science (9 papers)ANU Open Research (Australian National University) (3 papers)Uncertainty in Artificial Intelligence (1 paper)
- Partner nations
- CanadaUnited StatesAustralia
In The Last Decade
Tor Lattimore
42 papers receiving 825 citations
Tor Lattimore's Hit Papers
Peers
Comparison fields: 5 of 86
- Management Science and Operations Research 492
- Artificial Intelligence 464
- Computer Networks and Communications 207
- Computational Mathematics 4
- Computer Science Applications 32
Countries citing papers authored by Tor Lattimore
This map shows the geographic impact of Tor Lattimore'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 Tor Lattimore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tor Lattimore more than expected).
Fields of papers citing papers by Tor Lattimore
This network shows the impact of papers produced by Tor Lattimore. 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 Tor Lattimore. The network helps show where Tor Lattimore may publish in the future.
Co-authors
The 25 scholars most cited alongside Tor Lattimore, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 51 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Bandit Algorithms Hit paper breakdown → | 2020 | 526 |
| 2 | 2012 | 42 | |
| 3 | 2014 | 26 | |
| 4 | Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning | 2017 | 20 |
| 5 | Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities | 2017 | 18 |
| 6 | 2013 | 18 | |
| 7 | 2017 | 17 | |
| 8 | Causal bandits: learning good interventions via causal inference | 2016 | 16 |
| 9 | 2014 | 16 | |
| 10 | Refining the Confidence Level for Optimistic Bandit Strategies | 2018 | 13 |
| 11 | Linear multi-resource allocation with semi-bandit feedback | 2015 | 13 |
| 12 | 2011 | 12 | |
| 13 | 2013 | 12 | |
| 14 | TopRank: A practical algorithm for online stochastic ranking | 2018 | 10 |
| 15 | A Geometric Perspective on Optimal Representations for Reinforcement Learning | 2019 | 9 |
| 16 | Linear bandits with Stochastic Delayed Feedback | 2020 | 9 |
| 17 | 2011 | 7 | |
| 18 | 2014 | 7 | |
| 19 | An Information-Theoretic Approach to Minimax Regret in Partial Monitoring. | 2019 | 6 |
| 20 | Learning with Good Feature Representations in Bandits and in RL with a Generative Model | 2020 | 6 |
About Tor Lattimore
Tor Lattimore is a scholar working on Management Science and Operations Research, Artificial Intelligence, Computer Networks and Communications, Computational Theory and Mathematics and Electrical and Electronic Engineering, having authored 51 papers that have together received 848 indexed citations. Recurring topics across this work include Advanced Bandit Algorithms Research (39 papers), Machine Learning and Algorithms (22 papers), Optimization and Search Problems (12 papers), Reinforcement Learning in Robotics (12 papers), Auction Theory and Applications (7 papers), Computability, Logic, AI Algorithms (6 papers), Algorithms and Data Compression (4 papers) and Stochastic Gradient Optimization Techniques (3 papers). The work is most often cited by research in Management Science and Operations Research (492 citations), Artificial Intelligence (464 citations), Computer Networks and Communications (207 citations), Computational Mathematics (4 citations) and Computer Science Applications (32 citations). Tor Lattimore has collaborated with scholars based in Canada, United States and Australia. Frequent co-authors include Csaba Szepesvári, Marcus Hütter, Rémi Munos, András György, Christoph Dann, Emma Brunskill, Koby Crammer, Ruitong Huang, Mark D. Reid and Laurent Orseau. Their work appears in journals such as Theoretical Computer Science, Journal of Machine Learning Research, Lecture notes in computer science, ANU Open Research (Australian National University) and Uncertainty in Artificial Intelligence.
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.