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.
Distributional Reinforcement Learning With Quantile Regression
2018294 citationsWill Dabney, Mark Rowland 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 Mark Rowland'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 Mark Rowland with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Rowland more than expected).
This network shows the impact of papers produced by Mark Rowland. 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 Mark Rowland. The network helps show where Mark Rowland may publish in the future.
Co-authorship network of co-authors of Mark Rowland
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Rowland.
A scholar is included among the top collaborators of Mark Rowland 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 Mark Rowland. Mark Rowland is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Munos, Rémi, Julien Pérolat, Jean-Baptiste Lespiau, et al.. (2020). Fast computation of Nash Equilibria in Imperfect Information Games. International Conference on Machine Learning. 1. 7119–7129.1 indexed citations
3.
Omidshafiei, Shayegan, Karl Tuyls, Wojciech Marian Czarnecki, et al.. (2020). Navigating the Landscape of Games.. arXiv (Cornell University).2 indexed citations
Choromański, Krzysztof, et al.. (2019). Unifying Orthogonal Monte Carlo Methods. International Conference on Machine Learning. 1203–1212.4 indexed citations
Rowland, Mark, Marc G. Bellemare, Will Dabney, Rémi Munos, & Yee Whye Teh. (2018). An Analysis of Categorical Distributional Reinforcement Learning. International Conference on Artificial Intelligence and Statistics. 29–37.4 indexed citations
10.
Choromański, Krzysztof, Mark Rowland, Tamás Sarlós, et al.. (2018). The Geometry of Random Features. International Conference on Artificial Intelligence and Statistics. 1–9.3 indexed citations
11.
Rowland, Mark, Krzysztof Choromański, Aldo Pacchiano, et al.. (2018). Geometrically Coupled Monte Carlo Sampling. Cambridge University Engineering Department Publications Database. 31. 195–205.3 indexed citations
12.
Rowland, Mark, et al.. (2018). Implications of Traffic Sign Recognition (TSR) Systems for Road Operators.3 indexed citations
13.
Tripuraneni, Nilesh, Mark Rowland, Zoubin Ghahramani, & Richard E. Turner. (2017). Magnetic hamiltonian Monte Carlo. Cambridge University Engineering Department Publications Database. 3453–3461.13 indexed citations
14.
Rowland, Mark & Adrian Weller. (2017). Uprooting and Rerooting Higher-Order Graphical Models. Neural Information Processing Systems. 30. 209–218.1 indexed citations
15.
Choromański, Krzysztof, Mark Rowland, & Adrian Weller. (2017). The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings. Cambridge University Engineering Department Publications Database. 30. 219–228.7 indexed citations
16.
Rowland, Mark, Aldo Pacchiano, & Adrian Weller. (2017). Conditions beyond treewidth for tightness of higher-order LP relaxations. International Conference on Artificial Intelligence and Statistics. 10–18.1 indexed citations
17.
Weller, Adrian, Mark Rowland, & David Sontag. (2016). Tightness of LP Relaxations for Almost Balanced Models. Apollo (University of Cambridge). 47–55.5 indexed citations
18.
Kerr, P. L., J. Newby, M. K. Prasad, et al.. (2010). Recent Developments in Neutron Detection and Multiplicity Counting with Liquid Scintillator. University of North Texas Digital Library (University of North Texas).2 indexed citations
Abyzov, Alexander S., et al.. (2001). Influence of detector surface processing on detector performance. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 458(1-2). 248–253.21 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.