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 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).
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-authorship network of co-authors of Tor Lattimore
This figure shows the co-authorship network connecting the top 25 collaborators of Tor Lattimore.
A scholar is included among the top collaborators of Tor Lattimore 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 Tor Lattimore. Tor Lattimore 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
1.
Lattimore, Tor & András György. (2021). Mirror Descent and the Information Ratio. Conference on Learning Theory. 2965–2992.1 indexed citations
2.
O’Donoghue, Brendan, Tor Lattimore, & Ian Osband. (2021). Matrix games with bandit feedback. Uncertainty in Artificial Intelligence.1 indexed citations
3.
Duan, Yaqi, et al.. (2021). Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. International Conference on Machine Learning. 4063–4073.2 indexed citations
4.
Lattimore, Tor & András György. (2021). Improved Regret for Zeroth-Order Stochastic Convex Bandits. Conference on Learning Theory. 2938–2964.3 indexed citations
5.
Lattimore, Tor, et al.. (2020). Learning with Good Feature Representations in Bandits and in RL with a Generative Model. International Conference on Machine Learning. 1. 5662–5670.6 indexed citations
6.
Li, Shuai, Tor Lattimore, & Csaba Szepesvári. (2019). Online Learning to Rank with Features. International Conference on Machine Learning. 3856–3865.
7.
Kveton, Branislav, Tor Lattimore, Ilya Markov, et al.. (2019). BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback. UvA-DARE (University of Amsterdam). 196–206.1 indexed citations
8.
Lattimore, Tor & Csaba Szepesvári. (2019). An Information-Theoretic Approach to Minimax Regret in Partial Monitoring.. Conference on Learning Theory. 2111–2139.2 indexed citations
9.
Bellemare, Marc G., Will Dabney, Robert Dadashi, et al.. (2019). A Geometric Perspective on Optimal Representations for Reinforcement Learning. Neural Information Processing Systems. 32. 4358–4369.8 indexed citations
10.
Kveton, Branislav, Tor Lattimore, Ilya Markov, et al.. (2018). BubbleRank: Safe Online Learning to Rerank.. arXiv (Cornell University).1 indexed citations
11.
Lattimore, Tor. (2018). Refining the Confidence Level for Optimistic Bandit Strategies. Journal of Machine Learning Research. 19(20). 1–32.4 indexed citations
12.
Lattimore, Tor, Branislav Kveton, Shuai Li, & Csaba Szepesvári. (2018). TopRank: A practical algorithm for online stochastic ranking. Neural Information Processing Systems. 31. 3945–3954.8 indexed citations
13.
Orseau, Laurent, Levi H. S. Lelis, Tor Lattimore, & Théophane Weber. (2018). Single-Agent Policy Tree Search With Guarantees. Neural Information Processing Systems. 31. 3201–3211.3 indexed citations
14.
Dann, Christoph, Tor Lattimore, & Emma Brunskill. (2017). Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning. Neural Information Processing Systems. 30. 5713–5723.20 indexed citations
15.
Huang, Ruitong, Tor Lattimore, András György, & Csaba Szepesvári. (2017). Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities. Journal of Machine Learning Research. 18(145). 1–31.9 indexed citations
16.
Lattimore, Tor, et al.. (2016). Causal bandits: learning good interventions via causal inference. ANU Open Research (Australian National University). 29. 1189–1197.13 indexed citations
17.
Lattimore, Tor. (2016). Regret Analysis of the Finite-Horizon Gittins Index Strategy for Multi-Armed Bandits. Conference on Learning Theory. 1214–1245.1 indexed citations
18.
Lattimore, Tor, Koby Crammer, & Csaba Szepesvári. (2015). Linear multi-resource allocation with semi-bandit feedback. Neural Information Processing Systems. 28. 964–972.12 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.