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.
Finite-time Analysis of the Multiarmed Bandit Problem
This map shows the geographic impact of Peter Auer'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 Peter Auer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Auer more than expected).
This network shows the impact of papers produced by Peter Auer. 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 Peter Auer. The network helps show where Peter Auer may publish in the future.
Co-authorship network of co-authors of Peter Auer
This figure shows the co-authorship network connecting the top 25 collaborators of Peter Auer.
A scholar is included among the top collaborators of Peter Auer 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 Peter Auer. Peter Auer is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Auer, Peter & Chao-Kai Chiang. (2016). An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits. Conference on Learning Theory. 116–120.5 indexed citations
7.
Auer, Peter, Alexander Clark, & Thomas Zeugmann. (2016). Guest editors' foreword. Theoretical Computer Science. 650. 1–3.1 indexed citations
Seldin, Yevgeny, Csaba Szepesvári, Peter Auer, & Yasin Abbasi-Yadkori. (2013). Evaluation and Analysis of the Performance of the EXP3 Algorithm in Stochastic Environments. Max Planck Digital Library. 103–116.17 indexed citations
Auer, Peter, Stephen Kwek, Wolfgang Maass, & Manfred K. Warmuth. (2000). Learning of Depth Two Neural Networks with Constant Fan-in at the Hidden Nodes. Electronic colloquium on computational complexity. 7.2 indexed citations
17.
Auer, Peter, Peter Gilles, Jörg Peters, & Margret Selting. (2000). Intonation regionaler Varietäten des Deutschen. Vorstellung eines Forschungsprojekts. FreiDok plus (Universitätsbibliothek Freiburg).5 indexed citations
18.
Auer, Peter. (1996). From Context to Contextualization. RACO (Revistes Catalanes amb Accés Obert) (Consorci de Serveis Universitaris de Catalunya). 11–28.27 indexed citations
19.
Auer, Peter, Mark Herbster, & Manfred K. Warmuth. (1995). Exponentially many local minima for single neurons. Neural Information Processing Systems. 8. 316–322.58 indexed citations
20.
Auer, Peter, Alan S. Manne, & Oliver Yu. (1976). Nuclear power, coal, and energy conservation: with a note on the cost of a nuclear moratorium. Transactions of the American Nuclear Society. 23.1 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.