Arnak S. Dalalyan
- Statistics and Probability top 1%
- Artificial Intelligence top 10%
- Computational Mechanics top 10%
- Finance top 10%
- Computer Vision and Pattern Recognition
- Co-authors
- A. B. TsybakovJohannes LedererMohamed HebiriMarkus ReißRenaud KerivenAlexandre B. TsybakovNakahiro YoshidaYury A. Kutoyants
- Topics
- Statistical Methods and Inference (22 papers)Sparse and Compressive Sensing Techniques (10 papers)Advanced Statistical Methods and Models (9 papers)
In The Last Decade
Arnak S. Dalalyan
34 papers receiving 464 citations
Peers
Comparison fields: 5 of 71
- Statistics and Probability 321
- Artificial Intelligence 170
- Computational Mechanics 114
- Finance 52
- Computer Vision and Pattern Recognition 46
Countries citing papers authored by Arnak S. Dalalyan
This map shows the geographic impact of Arnak S. Dalalyan'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 Arnak S. Dalalyan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arnak S. Dalalyan more than expected).
Fields of papers citing papers by Arnak S. Dalalyan
This network shows the impact of papers produced by Arnak S. Dalalyan. 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 Arnak S. Dalalyan. The network helps show where Arnak S. Dalalyan may publish in the future.
Co-authorship network of co-authors of Arnak S. Dalalyan
This figure shows the co-authorship network connecting the top 25 collaborators of Arnak S. Dalalyan. A scholar is included among the top collaborators of Arnak S. Dalalyan 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 Arnak S. Dalalyan. Arnak S. Dalalyan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | A nonasymptotic law of iterated logarithm for general M-estimators | 3 |
| 3 | 3 | |
| 4 | 6 | |
| 5 | Rate-optimal estimation of p-dimensional linear functionals in a sparse Gaussian model | 2 |
| 6 | 5 | |
| 7 | 3 | |
| 8 | Theoretical guarantees for approximate sampling from a smooth and log-concave distribution | 3 |
| 9 | 11 | |
| 10 | 10 | |
| 11 | Permutation estimation and minimax matching thresholds | 1 |
| 12 | Mirror averaging with sparsity priors | 18 |
| 13 | 2 | |
| 14 | 46 | |
| 15 | 3 | |
| 16 | 3 | |
| 17 | 10 | |
| 18 | 6 | |
| 19 | L_1-Penalized Robust Estimation for a Class of Inverse Problems Arising in Multiview Geometry | 15 |
| 20 | Aggregation by exponential weighting, sharp oracle inequalities and sparsity | 8 |
About Arnak S. Dalalyan
Arnak S. Dalalyan is a scholar working on Statistics and Probability, Computational Mechanics and Finance, having authored 34 papers that have together received 484 indexed citations. Recurring topics across this work include Statistical Methods and Inference (22 papers), Sparse and Compressive Sensing Techniques (10 papers) and Advanced Statistical Methods and Models (9 papers). The work is most often cited by research in Statistics and Probability (321 citations), Computational Mechanics (114 citations) and Artificial Intelligence (170 citations). Arnak S. Dalalyan has collaborated with scholars based in France, Armenia and Russia. Frequent co-authors include A. B. Tsybakov, Johannes Lederer, Mohamed Hebiri, Markus Reiß, Renaud Keriven, Alexandre B. Tsybakov, Nakahiro Yoshida, Yury A. Kutoyants, T. V. Galstyan and Peter Sturm. Their work appears in journals such as Journal of the Royal Statistical Society Series B (Statistical Methodology), The Annals of Statistics and International Journal of Computer Vision.
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.