Tobias Scheffer
About
In The Last Decade
Tobias Scheffer
101 papers receiving 3.4k citations
Hit Papers
Peers
Comparison fields: 5 of 161
- Artificial Intelligence 2.0k
- Computer Vision and Pattern Recognition 877
- Information Systems 532
- Signal Processing 402
- Computer Networks and Communications 289
Countries citing papers authored by Tobias Scheffer
This map shows the geographic impact of Tobias Scheffer'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 Tobias Scheffer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tobias Scheffer more than expected).
Fields of papers citing papers by Tobias Scheffer
This network shows the impact of papers produced by Tobias Scheffer. 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 Tobias Scheffer. The network helps show where Tobias Scheffer may publish in the future.
Co-authorship network of co-authors of Tobias Scheffer
This figure shows the co-authorship network connecting the top 25 collaborators of Tobias Scheffer. A scholar is included among the top collaborators of Tobias Scheffer 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 Tobias Scheffer. Tobias Scheffer 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 | Learning to identify concise regular expressions that describe email campaigns | 9 |
| 3 | Bayesian Games for Adversarial Regression Problems | 21 |
| 4 | Static prediction games for adversarial learning problems | 97 |
| 5 | Learning to Identify Regular Expressions that Describe Email Campaigns | 5 |
| 6 | Active Comparison of Prediction Models | 2 |
| 7 | Nash Equilibria of Static Prediction Games | 40 |
| 8 | Localizing Bugs in Program Executions with Graphical Models | 11 |
| 9 | Transfer Learning by Distribution Matching for Targeted Advertising | 26 |
| 10 | Support vector machines for collective inference | 0 |
| 11 | 13 | |
| 12 | Proceedings of the 8th international conference on Discovery Science | 6 |
| 13 | AUC Maximizing Support Vector Learning | 42 |
| 14 | A Support Vector Machine classifier for gene name recognition | 1 |
| 15 | Lerning Hidden Markov Models for Information Extraction Actively from Partially Labeled Text. | 5 |
| 16 | Expected Error Analysis for Model Selection | 6 |
| 17 | International Conference on Machine Learning (ICML-99). | 365 |
| 18 | Estimating the expected error of empirical minimizers for model selection | 1 |
| 19 | Why Experimentation can be better than Perfect Guidance | 5 |
| 20 | Generation of Task-Specific Segmentation Procedures as a Model Selection Task | 1 |
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.