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.
A Probabilistic Theory of Pattern Recognition
19961.9k citationsLuc Devroye, László Györfi et al.profile →
Prediction, Learning, and Games
20061.6k citationsNicolò Cesa‐Bianchi, Gábor Lugosiprofile →
Concentration Inequalities
2013772 citationsStéphane Boucheron, Gábor Lugosi 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 Gábor Lugosi'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 Gábor Lugosi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gábor Lugosi more than expected).
This network shows the impact of papers produced by Gábor Lugosi. 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 Gábor Lugosi. The network helps show where Gábor Lugosi may publish in the future.
Co-authorship network of co-authors of Gábor Lugosi
This figure shows the co-authorship network connecting the top 25 collaborators of Gábor Lugosi.
A scholar is included among the top collaborators of Gábor Lugosi 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 Gábor Lugosi. Gábor Lugosi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Liu, Tongliang, Gábor Lugosi, Gergely Neu, & Dacheng Tao. (2017). Algorithmic stability and hypothesis complexity. UTS ePRESS (University of Technology Sydney). 2159–2167.3 indexed citations
4.
Seldin, Yevgeny & Gábor Lugosi. (2017). An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits. Research at the University of Copenhagen (University of Copenhagen).8 indexed citations
5.
Alamgir, Morteza, Gábor Lugosi, & Ulrike von Luxburg. (2014). Density-preserving quantization with application to graph downsampling. Conference on Learning Theory. 543–559.4 indexed citations
6.
Cesa‐Bianchi, Nicolò, Gábor Lugosi, Pierre Gaillard, & Gilles Stoltz. (2012). Mirror descent meets fixed share (and feels no regret. HAL (Le Centre pour la Communication Scientifique Directe).5 indexed citations
7.
Cesa‐Bianchi, Nicolò, Pierre Gaillard, Gábor Lugosi, & Gilles Stoltz. (2012). A New Look at Shifting Regret. arXiv (Cornell University).6 indexed citations
8.
Audibert, Jean-Yves, Sébastien Bubeck, & Gábor Lugosi. (2011). Minimax Policies for Combinatorial Prediction Games. HAL (Le Centre pour la Communication Scientifique Directe).1 indexed citations
9.
Lugosi, Gábor. (2010). Desigualtats de concentració. RACO (Revistes Catalanes amb Accés Obert) (Consorci de Serveis Universitaris de Catalunya). 24(2). 97–136.
György, András, Tamás Linder, & Gábor Lugosi. (2008). Efficient tracking of the best of many experts. SZTAKI Publication Repository (Hungarian Academy of Sciences).2 indexed citations
12.
György, András, et al.. (2008). On-Line Sequential Bin Packing. Journal of Machine Learning Research. 11(4). 447–109.7 indexed citations
Kulkarni, Sanjeev R., Gábor Lugosi, & Santosh S. Venkatesh. (2000). Learning pattern classification— a survey (invited paper). IEEE Press eBooks. 134–162.1 indexed citations
18.
Cesa‐Bianchi, Nicolò & Gábor Lugosi. (1999). Worst-Case Bounds for the Logarithmic Loss of Predictors. RECERCAT (Consorci de Serveis Universitaris de Catalunya).1 indexed citations
19.
Boucheron, Stéphane, Gábor Lugosi, & Pascal Massart. (1999). A Sharp Concentration Inequality with Applications. RECERCAT (Consorci de Serveis Universitaris de Catalunya).5 indexed citations
20.
Györfi, László, et al.. (1998). A simple randomized algorithm for consistent sequential prediction of ergodic time series. Repositori digital de la UPF (Universitat Pompeu Fabra).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.