Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms
Countries citing papers authored by Thomas G. Dietterich
Since
Specialization
Citations
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
20 of 20 papers shown
1.
Dietterich, Thomas G.. (2025). Learning and Reasoning. Lecture notes in computer science.
2.
Dietterich, Thomas G., et al.. (2018). Efficient Exploration for Constrained MDPs.. National Conference on Artificial Intelligence.2 indexed citations
3.
Fern, Alan, et al.. (2014). Active lmitation learning: formal and practical reductions to I.I.D. learning. Journal of Machine Learning Research. 15(1). 3925–3963.10 indexed citations
4.
Dietterich, Thomas G., et al.. (2008). Integrating multiple learning components through Markov logic. National Conference on Artificial Intelligence. 622–627.4 indexed citations
5.
Paasch, Robert, et al.. (2007). Product Innovation for Interdisciplinary Design Under Changing Requirements. Guidelines for a Decision Support Method Adapted to NPD Processes.10 indexed citations
6.
Shen, Jianqiang, Lida Li, & Thomas G. Dietterich. (2007). Real-time detection of task switches of desktop users. International Joint Conference on Artificial Intelligence. 2868–2873.23 indexed citations
7.
Stumpf, Simone, Margaret Burnett, & Thomas G. Dietterich. (2007). Improving Intelligent Assistants for Desktop Activities.. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 119–121.2 indexed citations
8.
Dietterich, Thomas G., et al.. (2003). Model-based policy gradient reinforcement learning. International Conference on Machine Learning. 776–783.12 indexed citations
9.
Valentini, Giorgio & Thomas G. Dietterich. (2003). Low bias bagged support vector machines. International Conference on Machine Learning. 752–759.46 indexed citations
10.
Dietterich, Thomas G., et al.. (2001). Stabilizing Value Function Approximation with the BFBP Algorithm. Neural Information Processing Systems. 14. 1587–1594.2 indexed citations
11.
Chown, Eric & Thomas G. Dietterich. (2000). A Divide and Conquer Approach to Learning from Prior Knowledge. International Conference on Machine Learning. 143–150.4 indexed citations
12.
Margineantu, Dragos D. & Thomas G. Dietterich. (1997). Pruning Adaptive Boosting. International Conference on Machine Learning. 211–218.360 indexed citations
13.
Shavlik, Jude, et al.. (1991). Readings in Machine Learning. Morgan Kaufmann Publishers Inc. eBooks.239 indexed citations
14.
Almuallim, Hussein & Thomas G. Dietterich. (1991). Learning with many irrelevant features. National Conference on Artificial Intelligence. 27(12). 547–552.403 indexed citations
Wettschereck, Dietrich & Thomas G. Dietterich. (1991). Improving the Performance of Radial Basis Function Networks by Learning Center Locations. Neural Information Processing Systems. 4. 1133–1140.103 indexed citations
17.
Flann, Nicholas S., et al.. (1988). An efficient ATMS for equivalence relations. National Conference on Artificial Intelligence. 182–187.2 indexed citations
18.
Flann, Nicholas S., et al.. (1987). Forward chaining logic programming with the ATMS. National Conference on Artificial Intelligence. 24–29.3 indexed citations
19.
Flann, Nicholas S. & Thomas G. Dietterich. (1986). Selecting appropriate representations for learning from examples. National Conference on Artificial Intelligence. 460–466.28 indexed citations
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.