Ben Taskar

11.2k total citations · 2 hit papers
91 papers, 6.5k citations indexed

About

Ben Taskar is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Ben Taskar has authored 91 papers receiving a total of 6.5k indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Artificial Intelligence, 32 papers in Computer Vision and Pattern Recognition and 6 papers in Signal Processing. Recurrent topics in Ben Taskar's work include Topic Modeling (24 papers), Natural Language Processing Techniques (20 papers) and Bayesian Modeling and Causal Inference (15 papers). Ben Taskar is often cited by papers focused on Topic Modeling (24 papers), Natural Language Processing Techniques (20 papers) and Bayesian Modeling and Causal Inference (15 papers). Ben Taskar collaborates with scholars based in United States, Portugal and France. Ben Taskar's co-authors include Daphne Koller, Carlos Guestrin, Dan Klein, Lise Getoor, Pieter Abbeel, Kuzman Ganchev, Ben Sapp, Michael I. Jordan, Percy Liang and Jennifer Gillenwater and has published in prestigious journals such as Bioinformatics, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Medical Imaging.

In The Last Decade

Ben Taskar

88 papers receiving 6.0k citations

Hit Papers

Max-Margin Markov Networks 2003 2026 2010 2018 2003 2007 250 500 750

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Ben Taskar 4.4k 2.1k 535 512 511 91 6.5k
Arthur Gretton 3.9k 0.9× 2.1k 1.0× 498 0.9× 678 1.3× 239 0.5× 116 7.5k
Le Song 2.8k 0.6× 2.2k 1.0× 324 0.6× 985 1.9× 373 0.7× 95 5.5k
Vikas Sindhwani 3.7k 0.9× 2.8k 1.3× 273 0.5× 534 1.0× 628 1.2× 71 6.0k
Yiu‐ming Cheung 2.7k 0.6× 3.5k 1.7× 273 0.5× 816 1.6× 361 0.7× 362 6.8k
Shenghuo Zhu 3.1k 0.7× 2.0k 0.9× 327 0.6× 500 1.0× 1.1k 2.1× 126 6.1k
Brian Kulis 3.4k 0.8× 5.0k 2.4× 292 0.5× 670 1.3× 315 0.6× 53 7.5k
Wang Xiang-rui 2.6k 0.6× 1.8k 0.8× 460 0.9× 444 0.9× 512 1.0× 12 4.8k
Koby Crammer 6.1k 1.4× 2.6k 1.2× 548 1.0× 554 1.1× 643 1.3× 96 8.1k
Francis Bach 3.3k 0.8× 3.0k 1.4× 427 0.8× 990 1.9× 295 0.6× 99 7.0k
Dengyong Zhou 3.7k 0.8× 2.7k 1.3× 533 1.0× 492 1.0× 1.7k 3.4× 43 6.9k

Countries citing papers authored by Ben Taskar

Since Specialization
Citations

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

Fields of papers citing papers by Ben Taskar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ben Taskar

This figure shows the co-authorship network connecting the top 25 collaborators of Ben Taskar. A scholar is included among the top collaborators of Ben Taskar 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 Ben Taskar. Ben Taskar 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.
Chen, Tianqi, Sameer Singh, Ben Taskar, & Carlos Guestrin. (2015). {Efficient Second-Order Gradient Boosting for Conditional Random Fields}. International Conference on Artificial Intelligence and Statistics. 147–155. 12 indexed citations
2.
London, Ben, Bert Huang, Ben Taskar, & Lise Getoor. (2014). {PAC-Bayesian Collective Stability}. International Conference on Artificial Intelligence and Statistics. 585–594. 7 indexed citations
3.
Kulesza, Alex, et al.. (2013). Nystrom Approximation for Large-Scale Determinantal Processes. International Conference on Artificial Intelligence and Statistics. 85–98. 18 indexed citations
4.
Fox, Emily B., et al.. (2013). Approximate Inference in Continuous Determinantal Processes. Neural Information Processing Systems. 26. 1430–1438. 21 indexed citations
5.
Li, Shen, Joäo Graça, & Ben Taskar. (2012). Wiki-ly Supervised Part-of-Speech Tagging. Empirical Methods in Natural Language Processing. 1389–1398. 55 indexed citations
6.
Gillenwater, Jennifer, Kuzman Ganchev, Joäo Graça, Fernando Pereira, & Ben Taskar. (2011). Posterior Sparsity in Unsupervised Dependency Parsing. Journal of Machine Learning Research. 12(14). 455–490. 26 indexed citations
7.
Graça, Joäo, Kuzman Ganchev, Luísa Coheur, Fernando Pereira, & Ben Taskar. (2011). Controlling complexity in part-of-speech induction. arXiv (Cornell University). 41(2). 527–551. 7 indexed citations
8.
Kulesza, Alex & Ben Taskar. (2011). k-DPPs: Fixed-Size Determinantal Point Processes. International Conference on Machine Learning. 1193–1200. 87 indexed citations
9.
Weiss, David J. & Ben Taskar. (2010). Stuctured Predictions Cascades. ScholarlyCommons (University of Pennsylvania). 1 indexed citations
10.
Ganchev, Kuzman, Joäo Graça, Jennifer Gillenwater, & Ben Taskar. (2010). Posterior Regularization for Structured Latent Variable Models. Journal of Machine Learning Research. 11(67). 2001–2049. 254 indexed citations
11.
Kulesza, Alex & Ben Taskar. (2010). Structured Determinantal Point Processes. ScholarlyCommons (University of Pennsylvania). 23. 1171–1179. 62 indexed citations
12.
Getoor, Lise & Ben Taskar. (2007). Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning). The MIT Press eBooks. 326 indexed citations breakdown →
13.
Taskar, Ben. (2007). Expectation Maximization, Posterior Constraints, and Statistical Alignment. Neural Information Processing Systems. 1 indexed citations
14.
Taskar, Ben, Simon Lacoste-Julien, & Michael I. Jordan. (2006). Structured Prediction, Dual Extragradient and Bregman Projections. Journal of Machine Learning Research. 7(60). 1627–1653. 61 indexed citations
15.
Taskar, Ben, Simon Lacoste-Julien, & Michael I. Jordan. (2005). Structured Prediction via the Extragradient Method. Neural Information Processing Systems. 18. 1345–1352. 39 indexed citations
16.
Taskar, Ben, Dan Klein, Michael J. Collins, Daphne Koller, & Christopher D. Manning. (2004). Max-Margin Parsing. Empirical Methods in Natural Language Processing. 1–8. 144 indexed citations
17.
Bartlett, Peter L., Michael Collins, Ben Taskar, & David McAllester. (2004). Exponentiated Gradient Algorithms for Large-margin Structured Classification. QUT ePrints (Queensland University of Technology). 17. 113–120. 45 indexed citations
18.
Taskar, Ben, et al.. (2003). Learning on the test data: leveraging Unseen features. International Conference on Machine Learning. 744–751. 16 indexed citations
19.
Taskar, Ben, et al.. (2003). Link Prediction in Relational Data. Neural Information Processing Systems. 16. 659–666. 262 indexed citations
20.
Taskar, Ben, Pieter Abbeel, & Daphne Koller. (2002). Discriminative probabilistic models for relational data. Uncertainty in Artificial Intelligence. 485–492. 394 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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