Tom Dietterich

5.3k total citations · 3 hit papers
20 papers, 3.3k citations indexed

About

Tom Dietterich is a scholar working on Artificial Intelligence, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Tom Dietterich has authored 20 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 3 papers in Computer Networks and Communications and 3 papers in Information Systems. Recurrent topics in Tom Dietterich's work include Machine Learning and Algorithms (6 papers), Reinforcement Learning in Robotics (4 papers) and Machine Learning and Data Classification (3 papers). Tom Dietterich is often cited by papers focused on Machine Learning and Algorithms (6 papers), Reinforcement Learning in Robotics (4 papers) and Machine Learning and Data Classification (3 papers). Tom Dietterich collaborates with scholars based in United States, Australia and India. Tom Dietterich's co-authors include Ghulum Bakiri, Ryszard S. Michalski, Alan Fern, Leslie Pack Kaelbling, Michael T. Rosenstein, Yann Dujardin, Iadine Chadès, Akshat Kumar, Daniel Sheldon and Lise Getoor and has published in prestigious journals such as ACM Computing Surveys, Journal of Artificial Intelligence Research and AI Magazine.

In The Last Decade

Tom Dietterich

20 papers receiving 3.1k citations

Hit Papers

align trajectories

What are hit papers?

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.

Solving Multiclass Learning Problems via Error-Correcting Output Codes

1995 1.7k citations Tom Dietterich, Ghulum Bakiri Journal of Artificial Intelligence Research profile →
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition

2000 797 citations Tom Dietterich Journal of Artificial Intelligence Research profile →
Overfitting and undercomputing in machine learning

1995 517 citations Tom Dietterich ACM Computing Surveys profile →

Peers

Countries citing papers authored by Tom Dietterich

Since Specialization

Citations

This map shows the geographic impact of Tom 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 Tom Dietterich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Dietterich more than expected).

Fields of papers citing papers by Tom Dietterich

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tom 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 Tom Dietterich. The network helps show where Tom Dietterich may publish in the future.

Co-authorship network of co-authors of Tom Dietterich

This figure shows the co-authorship network connecting the top 25 collaborators of Tom Dietterich. A scholar is included among the top collaborators of Tom 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 Tom Dietterich. Tom Dietterich is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Three New Algorithms to Solve N-POMDPs 2017 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Yann Dujardin, Tom Dietterich, Iadine Chadès	10
2	Who speaks for AI? 2016 ·Eric Eaton, Tom Dietterich, Maria Gini, Barbara J. Grosz, Charles L. Isbell, Subbarao Kambhampati, Michael L. Littman, Francesca Rossi, Stuart Russell, Peter Stone, Toby Walsh, Michael Wooldridge	1
3	α-min: a compact approximate solver for finite-horizon POMDPs 2015 ·International Conference on Artificial Intelligence ·Yann Dujardin, Tom Dietterich, Iadine Chadès	10
4	Letters to the Editor 2015 ·AI Magazine ·Stuart Russell, Tom Dietterich, Eric Horvitz, Bart Selman, Francesca Rossi, Demis Hassabis, Shane Legg, Mustafa Suleyman, Dileep George, (unknown)	7
5	Prune-and-Score: Learning for Greedy Coreference Resolution 2014 ·Chao Ma, Janardhan Rao Doppa, (unknown), Prashanth Mannem, Xiaoli Z. Fern, Tom Dietterich, Prasad Tadepalli	17
6	State Aggregation in Monte Carlo Tree Search 2014 ·Proceedings of the AAAI Conference on Artificial Intelligence ·(unknown), Alan Fern, Tom Dietterich	21
7	Approximate Inference in Collective Graphical Models 2013 ·Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University) ·Daniel Sheldon, (unknown), Akshat Kumar, Tom Dietterich	22
8	Inferring strategies from limited reconnaissance in real-time strategy games 2012 ·Uncertainty in Artificial Intelligence ·(unknown), (unknown), Tom Dietterich, Alan Fern	6
9	Machine learning for computational sustainability 2012 ·Tom Dietterich, (unknown), Rebecca Hutchinson, (unknown)	8
10	Learning Probabilistic Behavior Models in Real-Time Strategy Games 2011 ·Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment ·(unknown), (unknown), Alan Fern, Tom Dietterich, (unknown), (unknown)	55
11	The use of provenance in information retrieval 2007 ·City Research Online (City University London) ·Simone Stumpf, (unknown), Tom Dietterich	7
12	Reinforcement Matching Using Region Context 2006 ·Hongli Deng, Eric N. Mortensen, Linda G. Shapiro, Tom Dietterich	6
13	Transfer Learning with an Ensemble of Background Tasks 2005 ·Neural Information Processing Systems ·Michael T. Rosenstein, (unknown), Tom Dietterich, Leslie Pack Kaelbling	32
14	SRL2004 ICML 2004 Workshop on Statistical Relational Learning and its Connections to Other Fields 2004 ·Tom Dietterich, Lise Getoor	5
15	Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition breakdown → 2000 ·Journal of Artificial Intelligence Research ·Tom Dietterich	797
16	Machine learning 1996 ·ACM Computing Surveys ·Tom Dietterich	24
17	Solving Multiclass Learning Problems via Error-Correcting Output Codes breakdown → 1995 ·Journal of Artificial Intelligence Research ·Tom Dietterich, Ghulum Bakiri	1698
18	Overfitting and undercomputing in machine learning breakdown → 1995 ·ACM Computing Surveys ·Tom Dietterich	517
19	Bridging the gap between specification and implementation 1991 ·IEEE Expert ·Tom Dietterich	3
20	Learning to Predict Sequences 1985 ·George Mason University ·Tom Dietterich, Ryszard S. Michalski	41

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.

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