Steve Hanneke
- Artificial Intelligence top 2%
- Statistical and Nonlinear Physics top 5%
- Management Science and Operations Research top 5%
- Computer Networks and Communications top 10%
- Sociology and Political Science
- Co-authors
- Eric P. XingWenjie FuLin F. YangJaime CarbonellMaria-Florina BalcanJennifer Wortman VaughanFan GuoJennifer R. Wortman
- Topics
- Machine Learning and Algorithms (29 papers)Algorithms and Data Compression (12 papers)Machine Learning and Data Classification (9 papers)
- Cited by
- Statistical and Nonlinear PhysicsArtificial IntelligenceManagement Science and Operations Research
- Partner nations
- United StatesIsraelDenmark
In The Last Decade
Steve Hanneke
32 papers receiving 902 citations
Hit Papers
Peers
Comparison fields: 5 of 130
- Artificial Intelligence 561
- Statistical and Nonlinear Physics 240
- Management Science and Operations Research 93
- Computer Networks and Communications 81
- Sociology and Political Science 75
Countries citing papers authored by Steve Hanneke
This map shows the geographic impact of Steve Hanneke'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 Steve Hanneke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steve Hanneke more than expected).
Fields of papers citing papers by Steve Hanneke
This network shows the impact of papers produced by Steve Hanneke. 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 Steve Hanneke. The network helps show where Steve Hanneke may publish in the future.
Co-authorship network of co-authors of Steve Hanneke
This figure shows the co-authorship network connecting the top 25 collaborators of Steve Hanneke. A scholar is included among the top collaborators of Steve Hanneke 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 Steve Hanneke. Steve Hanneke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries | 1 |
| 4 | 4 | |
| 5 | VC Classes are Adversarially Robustly Learnable, but Only Improperly | 1 |
| 6 | Actively Avoiding Nonsense in Generative Models | 1 |
| 7 | 0 | |
| 8 | 1 | |
| 9 | A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes. | 4 |
| 10 | A compression technique for analyzing disagreement-based active learning | 4 |
| 11 | Minimax analysis of active learning | 15 |
| 12 | Statistical Learning under Nonstationary Mixing Processes | 1 |
| 13 | 72 | |
| 14 | Activized Learning with Uniform Classification Noise | 2 |
| 15 | Negative Results for Active Learning with Convex Losses | 5 |
| 16 | 51 | |
| 17 | Adaptive Rates of Convergence in Active Learning. | 20 |
| 18 | Network Completion and Survey Sampling | 18 |
| 19 | The True Sample Complexity of Active Learning. | 36 |
| 20 | 3 |
About Steve Hanneke
Steve Hanneke is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Statistical and Nonlinear Physics, having authored 34 papers that have together received 965 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (29 papers), Algorithms and Data Compression (12 papers) and Machine Learning and Data Classification (9 papers). The work is most often cited by research in Statistical and Nonlinear Physics (240 citations), Artificial Intelligence (561 citations) and Management Science and Operations Research (93 citations). Steve Hanneke has collaborated with scholars based in United States, Israel and Denmark. Frequent co-authors include Eric P. Xing, Wenjie Fu, Lin F. Yang, Jaime Carbonell, Maria-Florina Balcan, Jennifer Wortman Vaughan, Wenjie Fu, Fan Guo, Jennifer R. Wortman and Samory Kpotufe. Their work appears in journals such as The Annals of Statistics, Machine Learning and Journal of Machine Learning Research.
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.