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.
DeepStack: Expert-level artificial intelligence in heads-up no-limit poker
2017409 citationsNeil Burch, Nolan Bard et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Neil Burch'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 Neil Burch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Neil Burch more than expected).
This network shows the impact of papers produced by Neil Burch. 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 Neil Burch. The network helps show where Neil Burch may publish in the future.
Co-authorship network of co-authors of Neil Burch
This figure shows the co-authorship network connecting the top 25 collaborators of Neil Burch.
A scholar is included among the top collaborators of Neil Burch 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 Neil Burch. Neil Burch 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.
Burch, Neil & Robert C. Holte. (2021). Automatic Move Pruning Revisited. Proceedings of the International Symposium on Combinatorial Search. 3(1). 18–24.1 indexed citations
2.
Foerster, Jakob, Hui Song, Edward Hughes, et al.. (2019). Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. International Conference on Machine Learning. 1942–1951.12 indexed citations
Bard, Nolan, Michael Johanson, Neil Burch, & Michael Bowling. (2013). Online implicit agent modelling. Adaptive Agents and Multi-Agents Systems. 255–262.24 indexed citations
8.
Burch, Neil, Marc Lanctot, Duane Szafron, & Richard G. Gibson. (2012). Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions. Neural Information Processing Systems. 25. 1880–1888.11 indexed citations
9.
Sturtevant, Nathan, et al.. (2009). Memory-based heuristics for explicit state spaces. International Joint Conference on Artificial Intelligence. 609–614.71 indexed citations
10.
Bowling, Michael, Nolan Bard, Darse Billings, et al.. (2009). A demonstration of the Polaris poker system. Adaptive Agents and Multi-Agents Systems. 1391–1392.4 indexed citations
11.
Burch, Neil, et al.. (2008). Predicting the performance of IDA* with conditional distributions. National Conference on Artificial Intelligence. 381–386.5 indexed citations
12.
Zinkevich, Martin, Michael Bowling, & Neil Burch. (2007). A new algorithm for generating equilibria in massive zero-sum games. National Conference on Artificial Intelligence. 788–793.27 indexed citations
13.
Schaeffer, Jonathan, Yngvi Björnsson, Neil Burch, et al.. (2005). Solving checkers. International Joint Conference on Artificial Intelligence. 292–297.22 indexed citations
14.
Billings, Darse, Aaron Davidson, Neil Burch, et al.. (2004). Game tree search with adaptation in stochastic imperfect information games.1 indexed citations
15.
Billings, Darse, Neil Burch, Aaron Davidson, et al.. (2003). Approximating game-theoretic optimal strategies for full-scale poker. International Joint Conference on Artificial Intelligence. 661–668.129 indexed citations
Burch, Neil. (1965). Data Processing of Psychophysiological Recordings (Discussant: Harold W. Shipton). NASA Special Publication. 72. 165.1 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.