Dino Sejdinović
- Artificial Intelligence top 1%
- Electrical and Electronic Engineering top 10%
- Computer Vision and Pattern Recognition top 2%
- Signal Processing top 2%
- Computer Networks and Communications top 5%
- Co-authors
- Robert J. PiechockiAndreas MüllerArthur GrettonBharath K. SriperumbudurKenji FukumizuHeiko StrathmannDejan VukobratovićAngela Doufexi
- Topics
- Statistical Methods and Inference (18 papers)Error Correcting Code Techniques (13 papers)Gaussian Processes and Bayesian Inference (13 papers)
- Partner nations
- United KingdomUnited StatesAustralia
In The Last Decade
Dino Sejdinović
63 papers receiving 2.6k citations
Hit Papers
Peers
Comparison fields: 5 of 163
- Artificial Intelligence 903
- Electrical and Electronic Engineering 535
- Computer Vision and Pattern Recognition 464
- Signal Processing 447
- Computer Networks and Communications 419
Countries citing papers authored by Dino Sejdinović
This map shows the geographic impact of Dino Sejdinović'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 Dino Sejdinović with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dino Sejdinović more than expected).
Fields of papers citing papers by Dino Sejdinović
This network shows the impact of papers produced by Dino Sejdinović. 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 Dino Sejdinović. The network helps show where Dino Sejdinović may publish in the future.
Co-authorship network of co-authors of Dino Sejdinović
This figure shows the co-authorship network connecting the top 25 collaborators of Dino Sejdinović. A scholar is included among the top collaborators of Dino Sejdinović 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 Dino Sejdinović. Dino Sejdinović is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 11 | |
| 5 | 1 | |
| 6 | Towards a Unified Analysis of Random Fourier Features | 10 |
| 7 | 56 | |
| 8 | 18 | |
| 9 | Inter-domain Deep Gaussian Processes | 3 |
| 10 | Causal Inference via Kernel Deviance Measures | 3 |
| 11 | 16 | |
| 12 | Testing and learning on distributions with symmetric noise invariance | 1 |
| 13 | Deep Kernel Machines via the Kernel Reparametrization Trick | 1 |
| 14 | 3 | |
| 15 | Super-sampling with a reservoir | 1 |
| 16 | DR-ABC: approximate Bayesian computation with kernel-based distribution regression | 5 |
| 17 | Probabilistic Integration | 2 |
| 18 | 31st International Conference on Machine Learning, ICML 2014 | 74 |
| 19 | Optimal kernel choice for large-scale two-sample testsbreakdown → | 312 |
| 20 | 21 |
About Dino Sejdinović
Dino Sejdinović is a scholar working on Statistics and Probability, Computational Mathematics and Artificial Intelligence, having authored 68 papers that have together received 2.7k indexed citations. Recurring topics across this work include Statistical Methods and Inference (18 papers), Error Correcting Code Techniques (13 papers) and Gaussian Processes and Bayesian Inference (13 papers). The work is most often cited by research in Signal Processing (447 citations), Statistics and Probability (313 citations) and Artificial Intelligence (903 citations). Dino Sejdinović has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include Robert J. Piechocki, Andreas Müller, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu, Heiko Strathmann, Dejan Vukobratović, Angela Doufexi, Massimiliano Pontil and Sivaraman Balakrishnan. Their work appears in journals such as Nature, Nature Communications and Journal of the American Statistical Association.
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.