Dmitry Pechyony

482 total citations
12 papers, 222 citations indexed

About

Dmitry Pechyony is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Mechanics. According to data from OpenAlex, Dmitry Pechyony has authored 12 papers receiving a total of 222 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Computational Mechanics. Recurrent topics in Dmitry Pechyony's work include Machine Learning and Algorithms (9 papers), Machine Learning and ELM (4 papers) and Machine Learning and Data Classification (4 papers). Dmitry Pechyony is often cited by papers focused on Machine Learning and Algorithms (9 papers), Machine Learning and ELM (4 papers) and Machine Learning and Data Classification (4 papers). Dmitry Pechyony collaborates with scholars based in United States and Israel. Dmitry Pechyony's co-authors include Vladimir Vapnik, Ran El‐Yaniv, Akshay Vashist, Rauf Izmailov, Ashish Rastogi, Corinna Cortes, Mehryar Mohri, Rosie Jones, Roni Khardon and Elad Yom‐Tov and has published in prestigious journals such as Machine Learning, Pattern Recognition Letters and Investigative Radiology.

In The Last Decade

Dmitry Pechyony

12 papers receiving 215 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dmitry Pechyony United States	9	175	101	22	18	15	12	222
Carlos Domingo Japan	6	189 1.1×	58 0.6×	6 0.3×	11 0.6×	11 0.7×	12	250
Josip Djolonga Switzerland	7	123 0.7×	48 0.5×	12 0.5×	38 2.1×	5 0.3×	12	165
Thijs Laarhoven Netherlands	8	144 0.8×	119 1.2×	5 0.2×	7 0.4×	4 0.3×	13	236
Brendan McMahan United States	7	142 0.8×	34 0.3×	27 1.2×	32 1.8×	5 0.3×	9	200
Adam Woźnica Switzerland	7	87 0.5×	117 1.2×	10 0.5×	3 0.2×	3 0.2×	9	182
Joseph Sill United States	6	76 0.4×	29 0.3×	6 0.3×	22 1.2×	21 1.4×	10	137
Christopher Liaw Canada	5	98 0.6×	26 0.3×	23 1.0×	27 1.5×	13 0.9×	13	153
Siddharth Gopal United States	9	240 1.4×	105 1.0×	5 0.2×	6 0.3×	4 0.3×	11	303
Daniil Ryabko France	8	86 0.5×	31 0.3×	3 0.1×	23 1.3×	19 1.3×	31	137

Countries citing papers authored by Dmitry Pechyony

Since Specialization

Citations

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

Fields of papers citing papers by Dmitry Pechyony

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dmitry Pechyony

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

All Works

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12 of 12 papers shown

1.

Pechyony, Dmitry, et al.. (2013). Fast Optimization Algorithms for Solving SVM+. 9 indexed citations

2.

Pechyony, Dmitry, et al.. (2013). A joint optimization of incrementality and revenue to satisfy both advertiser and publisher. 123–124. 8 indexed citations

3.

Khardon, Roni, et al.. (2012). Generalization Bounds for Online Learning Algorithms with Pairwise Loss Functions. Conference on Learning Theory. 28 indexed citations

4.

Pechyony, Dmitry & Vladimir Vapnik. (2010). On the theory of learning with Privileged Information. Neural Information Processing Systems. 1894–1902. 37 indexed citations

5.

Pechyony, Dmitry & Vladimir Vapnik. (2010). On the Theory of Learnining with Privileged Information. Neural Information Processing Systems. 23. 1894–1902. 20 indexed citations

6.

Pechyony, Dmitry, Rauf Izmailov, Akshay Vashist, & Vladimir Vapnik. (2010). SMO-Style Algorithms for Learning Using Privileged Information.. Investigative Radiology. 19(2). 235–241. 37 indexed citations

7.

El‐Yaniv, Ran & Dmitry Pechyony. (2009). Transductive Rademacher Complexity and its Applications. Journal of Artificial Intelligence Research. 35. 193–234. 26 indexed citations

8.

El‐Yaniv, Ran, et al.. (2008). Repairing self-confident active–transductive learners using systematic exploration. Pattern Recognition Letters. 29(9). 1245–1251. 4 indexed citations

9.

El‐Yaniv, Ran, Dmitry Pechyony, & Elad Yom‐Tov. (2008). Better multiclass classification via a margin-optimized single binary problem. Pattern Recognition Letters. 29(14). 1954–1959. 11 indexed citations

10.

Cortes, Corinna, Mehryar Mohri, Dmitry Pechyony, & Ashish Rastogi. (2008). Stability of transductive regression algorithms. 176–183. 27 indexed citations

11.

El‐Yaniv, Ran, Dmitry Pechyony, & Vladimir Vapnik. (2008). Large margin vs. large volume in transductive learning. Machine Learning. 72(3). 173–188. 14 indexed citations

12.

El‐Yaniv, Ran, Dmitry Pechyony, & Elad Yom‐Tov. (2006). Superior Multi-Class Classification Through a Margin-Optimized Single Binary Problem. 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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