Maxim Raginsky

3.7k total citations · 1 hit paper
96 papers, 1.8k citations indexed

About

Maxim Raginsky is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computer Networks and Communications. According to data from OpenAlex, Maxim Raginsky has authored 96 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Artificial Intelligence, 30 papers in Electrical and Electronic Engineering and 26 papers in Computer Networks and Communications. Recurrent topics in Maxim Raginsky's work include Wireless Communication Security Techniques (17 papers), Advanced Bandit Algorithms Research (13 papers) and Machine Learning and Algorithms (12 papers). Maxim Raginsky is often cited by papers focused on Wireless Communication Security Techniques (17 papers), Advanced Bandit Algorithms Research (13 papers) and Machine Learning and Algorithms (12 papers). Maxim Raginsky collaborates with scholars based in United States, Türkiye and Israel. Maxim Raginsky's co-authors include Svetlana Lazebnik, Rebecca Willett, Aolin Xu, Igal Sason, Alexander Rakhlin, Roummel F. Marcia, Zachary T. Harmany, Angelia Nedić, Soomin Lee and Naci Saldı and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Automatic Control and IEEE Transactions on Information Theory.

In The Last Decade

Maxim Raginsky

93 papers receiving 1.8k citations

Hit Papers

Locality-sensitive binary codes from shift-invariant kernels 2009 2026 2014 2020 2009 100 200 300 400
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.

  1. Locality-sensitive binary codes from shift-invariant kernels
    2009 419 citations Maxim Raginsky, Svetlana Lazebnik profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maxim Raginsky United States 23 648 629 302 257 253 96 1.8k
Leonard J. Schulman United States 26 234 0.4× 1.1k 1.7× 504 1.7× 114 0.4× 351 1.4× 102 2.2k
Edo Liberty United States 18 269 0.4× 573 0.9× 160 0.5× 432 1.7× 122 0.5× 39 1.3k
Renato D. C. Monteiro United States 32 250 0.4× 739 1.2× 213 0.7× 1.6k 6.2× 235 0.9× 107 4.2k
Lek‐Heng Lim United States 19 345 0.5× 496 0.8× 140 0.5× 849 3.3× 160 0.6× 52 3.1k
Sivasankaran Rajamanickam United States 21 481 0.7× 306 0.5× 548 1.8× 371 1.4× 286 1.1× 90 2.0k
Assaf Naor United States 31 212 0.3× 428 0.7× 352 1.2× 179 0.7× 134 0.5× 125 2.6k
Richard Pollack United States 25 637 1.0× 346 0.6× 205 0.7× 539 2.1× 245 1.0× 76 3.0k
Quentin F. Stout United States 29 405 0.6× 380 0.6× 1.1k 3.7× 229 0.9× 432 1.7× 140 3.5k
Yu‐Hong Dai China 29 321 0.5× 412 0.7× 468 1.5× 1.2k 4.8× 656 2.6× 129 3.3k
B. F. Svaiter Brazil 38 327 0.5× 544 0.9× 98 0.3× 1.8k 7.1× 141 0.6× 118 5.8k

Countries citing papers authored by Maxim Raginsky

Since Specialization
Citations

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

Fields of papers citing papers by Maxim Raginsky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maxim Raginsky

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

All Works

20 of 20 papers shown
1.
Hellström, Fredrik, Giuseppe Durisi, Benjamin Guedj, & Maxim Raginsky. (2025). Generalization Bounds: Perspectives from Information Theory and PAC-Bayes. 18(1). 1–223. 4 indexed citations
2.
Raginsky, Maxim. (2024). A Variational Approach to Sampling in Diffusion Processes. 3323–3328. 1 indexed citations
3.
Duan, Lingjie, Jun Luo, C. M. Wong, et al.. (2023). August 2023 Conference Calendar [Conference Calendar]. IEEE Control Systems. 43(4). C3–C3.
4.
Tzen, Belinda, et al.. (2023). Variational Principles for Mirror Descent and Mirror Langevin Dynamics. IEEE Control Systems Letters. 7. 1542–1547. 1 indexed citations
5.
Raginsky, Maxim, et al.. (2020). Universal Simulation of Stable Dynamical Systems by Recurrent Neural Nets. 384–392. 6 indexed citations
6.
Saldı, Naci, Tamer Başar, & Maxim Raginsky. (2020). Markov-Nash equilibria in mean-field games with discounted cost. 43 indexed citations
7.
Saldı, Naci, Tamer Başar, & Maxim Raginsky. (2020). Approximate nash equilibria in partially observed stochastic games with mean-field interactions. 13 indexed citations
8.
Raginsky, Maxim, et al.. (2019). Universal Approximation of Input-Output Maps by Temporal Convolutional Nets. arXiv (Cornell University). 32. 14071–14081. 1 indexed citations
9.
Ma, Xiao, Maxim Raginsky, & A.C. Cangellaris. (2018). A machine learning methodology for inferring network S-parameters in the presence of variability. 1–4. 7 indexed citations
10.
Raginsky, Maxim, et al.. (2018). Sequential prediction with coded side information under logarithmic loss. 753–769.
11.
Raginsky, Maxim, et al.. (2017). Verilog-A compatible recurrent neural network model for transient circuit simulation. 1–3. 28 indexed citations
12.
Xu, Aolin & Maxim Raginsky. (2017). Information-theoretic analysis of generalization capability of learning algorithms. arXiv (Cornell University). 30. 2524–2533. 38 indexed citations
13.
Raginsky, Maxim & Igal Sason. (2013). Concentration of Measure Inequalities in Information Theory, Communications, and Coding. 10(1-2). 1–247. 92 indexed citations
14.
Raginsky, Maxim & Alexander Rakhlin. (2011). Lower Bounds for Passive and Active Learning. ScholarlyCommons (University of Pennsylvania). 24. 1026–1034. 18 indexed citations
15.
Raginsky, Maxim. (2009). Achievability results for statistical learning under communication constraints. 2. 1328–1332. 1 indexed citations
16.
Lazebnik, Svetlana & Maxim Raginsky. (2009). Supervised Learning of Quantizer Codebooks by Information Loss Minimization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31(7). 1294–1309. 149 indexed citations
17.
Lazebnik, Svetlana & Maxim Raginsky. (2007). Learning Nearest-Neighbor Quantizers from Labeled Data by Information Loss Minimization.. International Conference on Artificial Intelligence and Statistics. 251–258. 12 indexed citations
18.
Raginsky, Maxim & Thomas J. Anastasio. (2007). Cooperation in self-organizing map networks enhances information transmission in the presence of input background activity. Biological Cybernetics. 98(3). 195–211. 2 indexed citations
19.
Raginsky, Maxim & Svetlana Lazebnik. (2005). Estimation of Intrinsic Dimensionality Using High-Rate Vector Quantization. Neural Information Processing Systems. 18. 1105–1112. 25 indexed citations
20.
Belavkin, V. P., Giacomo Mauro D’Ariano, & Maxim Raginsky. (2005). Operational distance and fidelity for quantum channels. Journal of Mathematical Physics. 46(6). 30 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Leonard J. Schulman Breakdown of academic impact, for papers by Edo Liberty Breakdown of academic impact, for papers by Renato D. C. Monteiro Breakdown of academic impact, for papers by Lek‐Heng Lim Breakdown of academic impact, for papers by Sivasankaran Rajamanickam Breakdown of academic impact, for papers by Assaf Naor Breakdown of academic impact, for papers by Richard Pollack Breakdown of academic impact, for papers by Quentin F. Stout Breakdown of academic impact, for papers by Yu‐Hong Dai Breakdown of academic impact, for papers by B. F. Svaiter
Rankless by CCL
2026