Filip Hanzely

875 total citations
8 papers, 63 citations indexed

About

Filip Hanzely is a scholar working on Artificial Intelligence, Computational Mechanics and Computer Networks and Communications. According to data from OpenAlex, Filip Hanzely has authored 8 papers receiving a total of 63 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 3 papers in Computational Mechanics and 1 paper in Computer Networks and Communications. Recurrent topics in Filip Hanzely's work include Stochastic Gradient Optimization Techniques (5 papers), Sparse and Compressive Sensing Techniques (3 papers) and Gaussian Processes and Bayesian Inference (1 paper). Filip Hanzely is often cited by papers focused on Stochastic Gradient Optimization Techniques (5 papers), Sparse and Compressive Sensing Techniques (3 papers) and Gaussian Processes and Bayesian Inference (1 paper). Filip Hanzely collaborates with scholars based in Saudi Arabia, United Kingdom and Russia. Filip Hanzely's co-authors include Peter Richtárik, Anna Krakovská, Lin Xiao, Eduard Gorbunov, Konstantin Mishchenko, Samuel Horváth, Jingwei Liang and Dmitry Kovalev and has published in prestigious journals such as IEEE Transactions on Signal Processing, Physical review. E and Computational Optimization and Applications.

In The Last Decade

Filip Hanzely

8 papers receiving 61 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Filip Hanzely Saudi Arabia	5	27	16	11	11	10	8	63
Felix Lenders Germany	3	9 0.3×	13 0.8×	2 0.2×	3 0.3×	12 1.2×	4	47
Nilesh Tripuraneni United States	6	72 2.7×	19 1.2×	2 0.2×	6 0.5×	7 0.7×	11	85
Jonathan Weed United States	5	24 0.9×	5 0.3×	2 0.2×	2 0.2×	13 1.3×	10	94
Kofi P. Adragni United States	6	35 1.3×	4 0.3×	4 0.4×	3 0.3×	7 0.7×	10	100
Tai Qin United States	3	46 1.7×	4 0.3×	10 0.9×	76 6.9×	4 0.4×	3	107
Majid Janzamin United States	7	28 1.0×	20 1.3×		3 0.3×	6 0.6×	11	79
Xiyu Zhai United States	5	78 2.9×	16 1.0×		11 1.0×	4 0.4×	5	95
Theodor Misiakiewicz United States	5	37 1.4×	16 1.0×	2 0.2×	10 0.9×	4 0.4×	7	60
Gary Bécigneul Switzerland	5	47 1.7×	11 0.7×		7 0.6×	7 0.7×	8	63

Countries citing papers authored by Filip Hanzely

Since Specialization

Citations

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

Fields of papers citing papers by Filip Hanzely

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Filip Hanzely

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

All Works

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8 of 8 papers shown

Hanzely, Filip, Peter Richtárik, & Lin Xiao. (2021). Accelerated Bregman proximal gradient methods for relatively smooth convex optimization. Computational Optimization and Applications. 79(2). 405–440. 18 indexed citations

Mishchenko, Konstantin, Filip Hanzely, & Peter Richtárik. (2020). 99% of Worker-Master Communication in Distributed Optimization Is Not Needed.. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 979–988. 3 indexed citations

Hanzely, Filip, Dmitry Kovalev, & Peter Richtárik. (2020). Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 1. 4039–4048. 1 indexed citations

Hanzely, Filip, et al.. (2020). Lower Bounds and Optimal Algorithms for Personalized Federated Learning. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 33. 2304–2315. 2 indexed citations

Hanzely, Filip, et al.. (2020). Best Pair Formulation & Accelerated Scheme for Non-Convex Principal Component Pursuit. IEEE Transactions on Signal Processing. 68. 6128–6141. 4 indexed citations

Gorbunov, Eduard, et al.. (2020). Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR 108. 11 indexed citations

Hanzely, Filip, Konstantin Mishchenko, & Peter Richtárik. (2018). SEGA: Variance Reduction via Gradient Sketching. King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology). 31. 2082–2093. 4 indexed citations

Krakovská, Anna & Filip Hanzely. (2016). Testing for causality in reconstructed state spaces by an optimized mixed prediction method. Physical review. E. 94(5). 52203–52203. 20 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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