Stéphane Boucheron

3.9k total citations · 1 hit paper
22 papers, 1.7k citations indexed

About

Stéphane Boucheron is a scholar working on Artificial Intelligence, Statistics and Probability and Mathematical Physics. According to data from OpenAlex, Stéphane Boucheron has authored 22 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 9 papers in Statistics and Probability and 4 papers in Mathematical Physics. Recurrent topics in Stéphane Boucheron's work include Machine Learning and Algorithms (6 papers), Statistical Methods and Inference (5 papers) and Bayesian Methods and Mixture Models (4 papers). Stéphane Boucheron is often cited by papers focused on Machine Learning and Algorithms (6 papers), Statistical Methods and Inference (5 papers) and Bayesian Methods and Mixture Models (4 papers). Stéphane Boucheron collaborates with scholars based in France, Spain and Australia. Stéphane Boucheron's co-authors include Gábor Lugosi, Pascal Massart, Olivier Bousquet, Peter L. Bartlett, Yves Grandvalet, Stéphane Canu, Kavé Salamatian, Christian Léonard, Fabrice Gamboa and Jean Sallantin and has published in prestigious journals such as IEEE Transactions on Information Theory, Neural Computation and Machine Learning.

In The Last Decade

Stéphane Boucheron

21 papers receiving 1.6k citations

Hit Papers

align trajectories

What are hit papers?

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.

Concentration Inequalities

2013 772 citations Stéphane Boucheron, Gábor Lugosi et al. Oxford University Press eBooks profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Stéphane Boucheron France	12	785	565	207	198	192	22	1.7k
Pierre Priouret France	9	682 0.9×	329 0.6×	135 0.7×	296 1.5×	236 1.2×	17	1.7k
Bharath K. Sriperumbudur United States	19	1.2k 1.5×	593 1.0×	492 2.4×	228 1.2×	180 0.9×	50	2.5k
Ingo Steinwart United States	25	1.1k 1.5×	609 1.1×	588 2.8×	519 2.6×	122 0.6×	65	2.2k
Ioannis Kontoyiannis United States	21	620 0.8×	314 0.6×	195 0.9×	49 0.2×	272 1.4×	102	1.5k
Chii-Ruey Hwang Taiwan	15	471 0.6×	506 0.9×	108 0.5×	68 0.3×	91 0.5×	39	1.3k
Yihong Wu United States	25	666 0.8×	470 0.8×	234 1.1×	407 2.1×	476 2.5×	84	1.9k
Flemming Topsøe Denmark	16	498 0.6×	194 0.3×	150 0.7×	54 0.3×	97 0.5×	53	1.4k
Vladimir Koltchinskii United States	23	925 1.2×	922 1.6×	374 1.8×	624 3.2×	104 0.5×	52	2.1k
Paul C. Shields United States	20	869 1.1×	252 0.4×	211 1.0×	69 0.3×	256 1.3×	70	1.9k
Mark Rudelson United States	20	455 0.6×	676 1.2×	290 1.4×	1.0k 5.3×	169 0.9×	46	2.3k

Countries citing papers authored by Stéphane Boucheron

Since Specialization

Citations

This map shows the geographic impact of Stéphane Boucheron'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 Stéphane Boucheron with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stéphane Boucheron more than expected).

Fields of papers citing papers by Stéphane Boucheron

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stéphane Boucheron. 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 Stéphane Boucheron. The network helps show where Stéphane Boucheron may publish in the future.

Co-authorship network of co-authors of Stéphane Boucheron

This figure shows the co-authorship network connecting the top 25 collaborators of Stéphane Boucheron. A scholar is included among the top collaborators of Stéphane Boucheron 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 Stéphane Boucheron. Stéphane Boucheron is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Boucheron, Stéphane, et al.. (2015). Tail index estimation, concentration and adaptivity. Electronic Journal of Statistics. 9(2). 9 indexed citations

2.

Boucheron, Stéphane, et al.. (2015). About Adaptive Coding on Countable Alphabets: Max-Stable Envelope Classes. IEEE Transactions on Information Theory. 61(9). 4948–4967. 6 indexed citations

3.

Boucheron, Stéphane, et al.. (2014). About adaptive coding on countable alphabets. 10 indexed citations

4.

Boucheron, Stéphane, Gábor Lugosi, & Pascal Massart. (2013). Concentration Inequalities. Oxford University Press eBooks. 772 indexed citations breakdown →

5.

Boucheron, Stéphane & Pascal Massart. (2010). A high-dimensional Wilks phenomenon. Probability Theory and Related Fields. 150(3-4). 405–433. 10 indexed citations

6.

Boucheron, Stéphane, Gábor Lugosi, & Pascal Massart. (2009). On concentration of self-bounding functions. Electronic Journal of Probability. 14(none). 17 indexed citations

7.

Boucheron, Stéphane, et al.. (2009). A Bernstein-Von Mises Theorem for discrete probability distributions. Electronic Journal of Statistics. 3(none). 17 indexed citations

8.

Boucheron, Stéphane, Olivier Bousquet, & Gábor Lugosi. (2005). Theory of Classification: a Survey of Some Recent Advances. ESAIM Probability and Statistics. 9. 323–375. 224 indexed citations

9.

Boucheron, Stéphane, et al.. (2003). Optimal error exponents in hidden markov models order estimation. IEEE Transactions on Information Theory. 49(4). 964–980. 28 indexed citations

10.

Boucheron, Stéphane, Gábor Lugosi, & Pascal Massart. (2003). Concentration inequalities using the entropy method. The Annals of Probability. 31(3). 96 indexed citations

11.

Boucheron, Stéphane, Fabrice Gamboa, & Christian Léonard. (2002). Bins and balls; Large deviations of the empirical occupancy process. The Annals of Applied Probability. 12(2). 10 indexed citations

12.

Bartlett, Peter L., Stéphane Boucheron, & Gábor Lugosi. (2002). Model Selection and Error Estimation. Machine Learning. 48(1-3). 85–113. 181 indexed citations

13.

Boucheron, Stéphane, et al.. (2001). On the Independence Number of Random Interval Graphs. Combinatorics Probability Computing. 10(5). 385–396. 2 indexed citations

14.

Boucheron, Stéphane & Kavé Salamatian. (2000). About priority encoding transmission. IEEE Transactions on Information Theory. 46(2). 699–705. 34 indexed citations

15.

Boucheron, Stéphane, et al.. (2000). On the Fluctuations of the Giant Component. Combinatorics Probability Computing. 9(4). 287–304. 19 indexed citations

16.

Boucheron, Stéphane, Gábor Lugosi, & Pascal Massart. (1999). A Sharp Concentration Inequality with Applications. RECERCAT (Consorci de Serveis Universitaris de Catalunya). 5 indexed citations

17.

Boucheron, Stéphane, et al.. (1997). An urn model from learning theory. Random Structures and Algorithms. 10(1-2). 43–67. 11 indexed citations

18.

Grandvalet, Yves, Stéphane Canu, & Stéphane Boucheron. (1997). Noise Injection: Theoretical Prospects. Neural Computation. 9(5). 1093–1108. 91 indexed citations

19.

Boucheron, Stéphane, et al.. (1997). Codage à protections inégales et diffusion. 1 indexed citations

20.

Boucheron, Stéphane & Jean Sallantin. (1988). Learnability in the Presence of Noise.. 25–35. 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.

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