Thomas G. Dietterich
- Artificial Intelligence top 0.05%
- Computer Vision and Pattern Recognition top 0.1%
- Information Systems top 0.2%
- Computational Theory and Mathematics top 0.5%
- Signal Processing top 0.5%
- Co-authors
- Richard H. LathropTomás Lozano‐PérezTodd K. LeenVolker TrespHussein AlmuallimDragos D. MargineantuRyszard S. MichalskiDietrich Wettschereck
- Topics
- Machine Learning and Algorithms (24 papers)Machine Learning and Data Classification (21 papers)Reinforcement Learning in Robotics (17 papers)
- Partner nations
- United StatesAustraliaGermany
In The Last Decade
Thomas G. Dietterich
164 papers receiving 14.1k citations
Hit Papers
Peers
Comparison fields: 5 of 218
- Artificial Intelligence 8.5k
- Computer Vision and Pattern Recognition 3.9k
- Information Systems 1.8k
- Computational Theory and Mathematics 1.1k
- Signal Processing 1.1k
Countries citing papers authored by Thomas G. Dietterich
This map shows the geographic impact of Thomas G. Dietterich'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 Thomas G. Dietterich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas G. Dietterich more than expected).
Fields of papers citing papers by Thomas G. Dietterich
This network shows the impact of papers produced by Thomas G. Dietterich. 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 Thomas G. Dietterich. The network helps show where Thomas G. Dietterich may publish in the future.
Co-authorship network of co-authors of Thomas G. Dietterich
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas G. Dietterich. A scholar is included among the top collaborators of Thomas G. Dietterich 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 Thomas G. Dietterich. Thomas G. Dietterich is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Transductive optimization of top k precision | 6 |
| 2 | PAC optimal MDP planning with application to invasive species management | 10 |
| 3 | Learnability of the Superset Label Learning Problem | 29 |
| 4 | Towards learning rules from natural texts | 4 |
| 5 | Machine learning in ecosystem informatics and sustainability | 21 |
| 6 | Gradient Tree Boosting for Training Conditional Random Fields | 21 |
| 7 | Real-time detection of task switches of desktop users | 23 |
| 8 | Improving Intelligent Assistants for Desktop Activities. | 2 |
| 9 | The TaskTracer system | 10 |
| 10 | Low bias bagged support vector machines | 46 |
| 11 | Data mining for manufacturing control: an application in optimizing IC tests | 5 |
| 12 | Action Refinement in Reinforcement Learning by Probability Smoothing | 1 |
| 13 | Stabilizing Value Function Approximation with the BFBP Algorithm | 2 |
| 14 | A Divide and Conquer Approach to Learning from Prior Knowledge | 4 |
| 15 | Pruning Adaptive Boosting | 360 |
| 16 | Hierarchical Explanation-Based Reinforcement Learning | 11 |
| 17 | A reinforcement learning approach to job-shop scheduling | 253 |
| 18 | Memory-Based Methods for Regression and Classification | 9 |
| 19 | Forward chaining logic programming with the ATMS | 3 |
| 20 | Selecting appropriate representations for learning from examples | 28 |
About Thomas G. Dietterich
Thomas G. Dietterich is a scholar working on Artificial Intelligence, Ecological Modeling and Information Systems and Management, having authored 167 papers that have together received 15.3k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (24 papers), Machine Learning and Data Classification (21 papers) and Reinforcement Learning in Robotics (17 papers). The work is most often cited by research in Artificial Intelligence (8.5k citations), Computer Vision and Pattern Recognition (3.9k citations) and Signal Processing (1.1k citations). Thomas G. Dietterich has collaborated with scholars based in United States, Australia and Germany. Frequent co-authors include Richard H. Lathrop, Tomás Lozano‐Pérez, Todd K. Leen, Volker Tresp, Hussein Almuallim, Dragos D. Margineantu, Ryszard S. Michalski, Dietrich Wettschereck, Wei Zhang and Giorgio Valentini. Their work appears in journals such as Communications of the ACM, Ecological Economics and Pattern Recognition.
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.