Vitaly Kuznetsov
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Management Science and Operations Research
- Signal Processing
- Control and Systems Engineering
- Co-authors
- Mehryar MohriUmar SyedCorinna CortesScott Cheng‐Hsin YangHank LiaoBrian RoarkMichael RileyPrasoon Goyal
- Topics
- Machine Learning and Algorithms (8 papers)Domain Adaptation and Few-Shot Learning (4 papers)Natural Language Processing Techniques (2 papers)
- Journals
- Machine LearningJournal of Machine Learning ResearchThe Journal of Difference Equations and Applications
- Partner nations
- United StatesSwitzerlandChina
In The Last Decade
Vitaly Kuznetsov
15 papers receiving 147 citations
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 99
- Computer Vision and Pattern Recognition 29
- Management Science and Operations Research 27
- Signal Processing 17
- Control and Systems Engineering 16
Countries citing papers authored by Vitaly Kuznetsov
This map shows the geographic impact of Vitaly Kuznetsov'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 Vitaly Kuznetsov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vitaly Kuznetsov more than expected).
Fields of papers citing papers by Vitaly Kuznetsov
This network shows the impact of papers produced by Vitaly Kuznetsov. 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 Vitaly Kuznetsov. The network helps show where Vitaly Kuznetsov may publish in the future.
Co-authorship network of co-authors of Vitaly Kuznetsov
This figure shows the co-authorship network connecting the top 25 collaborators of Vitaly Kuznetsov. A scholar is included among the top collaborators of Vitaly Kuznetsov 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 Vitaly Kuznetsov. Vitaly Kuznetsov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 18 | |
| 2 | Foundations of Sequence-to-Sequence Modeling for Time Series | 12 |
| 3 | Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses | 2 |
| 4 | Discriminative State Space Models | 1 |
| 5 | Multi-Armed Bandits with Non-Stationary Rewards | 2 |
| 6 | Time series prediction and online learning | 5 |
| 7 | Structured Prediction Theory Based on Factor Graph Complexity | 5 |
| 8 | 31 | |
| 9 | 11 | |
| 10 | Structural Maxent Models | 1 |
| 11 | Learning theory and algorithms for forecasting non-stationary time series | 19 |
| 12 | Kernel Extraction via Voted Risk Minimization | 2 |
| 13 | Multi-Class Deep Boosting | 33 |
| 14 | 14 | |
| 15 | 3 | |
| 16 | 4 |
About Vitaly Kuznetsov
Vitaly Kuznetsov is a scholar working on Artificial Intelligence, Discrete Mathematics and Combinatorics and Signal Processing, having authored 16 papers that have together received 163 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (8 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Natural Language Processing Techniques (2 papers). The work is most often cited by research in Artificial Intelligence (99 citations), Management Science and Operations Research (27 citations) and Signal Processing (17 citations). Vitaly Kuznetsov has collaborated with scholars based in United States, Switzerland and China. Frequent co-authors include Mehryar Mohri, Umar Syed, Corinna Cortes, Scott Cheng‐Hsin Yang, Hank Liao, Brian Roark, Michael Riley, Prasoon Goyal, Stephen M. Tanny and Giulia DeSalvo. Their work appears in journals such as Machine Learning, Journal of Machine Learning Research and The Journal of Difference Equations and Applications.
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.