David Tolpin

440 total citations
10 papers, 130 citations indexed

About

David Tolpin is a scholar working on Artificial Intelligence, Computer Networks and Communications and Management Science and Operations Research. According to data from OpenAlex, David Tolpin has authored 10 papers receiving a total of 130 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 4 papers in Computer Networks and Communications and 3 papers in Management Science and Operations Research. Recurrent topics in David Tolpin's work include Bayesian Modeling and Causal Inference (4 papers), AI-based Problem Solving and Planning (4 papers) and Advanced Bandit Algorithms Research (3 papers). David Tolpin is often cited by papers focused on Bayesian Modeling and Causal Inference (4 papers), AI-based Problem Solving and Planning (4 papers) and Advanced Bandit Algorithms Research (3 papers). David Tolpin collaborates with scholars based in Israel, United Kingdom and Mexico. David Tolpin's co-authors include Solomon Eyal Shimony, Ariel Felner, Jan-Willem van de Meent, Hongseok Yang, Frank Wood, Roni Stern, Guni Sharon, Eli Boyarski, Stuart Russell and Erez Karpas and has published in prestigious journals such as Artificial Intelligence, IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) and Intelligent Decision Technologies.

In The Last Decade

David Tolpin

9 papers receiving 126 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
David Tolpin Israel	6	81	54	32	18	14	10	130
Christophe Gonzales France	7	28 0.3×	20 0.4×	15 0.5×	27 1.5×	16 1.1×	22	105
Karl Pfleger United States	5	117 1.4×	23 0.4×	21 0.7×	10 0.6×	27 1.9×	7	166
Silvia Richter Australia	4	129 1.6×	57 1.1×	63 2.0×	4 0.2×	10 0.7×	4	147
Abid M. Malik United States	8	37 0.5×	31 0.6×	82 2.6×	7 0.4×	6 0.4×	26	164
Yi-Qi Hu China	8	116 1.4×	28 0.5×	9 0.3×	27 1.5×	45 3.2×	13	178
Pierre Genevès France	8	104 1.3×	19 0.4×	64 2.0×	6 0.3×	32 2.3×	33	155
Robert Mattmüller Germany	10	268 3.3×	36 0.7×	72 2.3×	11 0.6×	65 4.6×	29	302
Daniel J. Fremont United States	6	68 0.8×	14 0.3×	15 0.5×	6 0.3×	22 1.6×	10	118
Shaull Almagor Israel	7	86 1.1×	46 0.9×	13 0.4×	22 1.2×	71 5.1×	22	138
Ximeng Sun United States	7	55 0.7×	84 1.6×	16 0.5×	4 0.2×	4 0.3×	11	142

Countries citing papers authored by David Tolpin

Since Specialization

Citations

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

Fields of papers citing papers by David Tolpin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Tolpin

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

All Works

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10 of 10 papers shown

1.

Brafman, Ronen I., et al.. (2023). Probabilistic Programs as an Action Description Language. Proceedings of the AAAI Conference on Artificial Intelligence. 37(13). 15351–15358. 3 indexed citations

2.

Tolpin, David & Solomon Eyal Shimony. (2021). MCTS Based on Simple Regret. Proceedings of the AAAI Conference on Artificial Intelligence. 26(1). 570–576. 3 indexed citations

3.

Tolpin, David & Frank Wood. (2021). Maximum a Posteriori Estimation by Search in Probabilistic Programs. Proceedings of the International Symposium on Combinatorial Search. 6(1). 201–205.

4.

Boyarski, Eli, et al.. (2021). ICBS: The Improved Conflict-Based Search Algorithm for Multi-Agent Pathfinding. Proceedings of the International Symposium on Combinatorial Search. 6(1). 223–225. 51 indexed citations

5.

Karpas, Erez, et al.. (2017). Rational deployment of multiple heuristics in optimal state-space search. Artificial Intelligence. 256. 181–210. 7 indexed citations

6.

Tolpin, David, Jan-Willem van de Meent, Hongseok Yang, & Frank Wood. (2016). Design and Implementation of Probabilistic Programming Language Anglican. 1–12. 27 indexed citations

7.

Tolpin, David, et al.. (2013). Towards Rational Deployment of Multiple Heuristics in A*. arXiv (Cornell University). 674–680. 8 indexed citations

8.

Tolpin, David & Solomon Eyal Shimony. (2012). Rational value of information estimation for measurement selection. Intelligent Decision Technologies. 6(4). 297–304. 2 indexed citations

9.

Russell, Stuart, et al.. (2012). Selecting Computations: Theory and Applications. arXiv (Cornell University). 346–355. 24 indexed citations

10.

Tolpin, David & Solomon Eyal Shimony. (2011). Semimyopic Measurement Selection for Optimization Under Uncertainty. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 42(2). 565–579. 5 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.

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