Ben Taskar
About
In The Last Decade
Ben Taskar
88 papers receiving 6.0k citations
Hit Papers
Peers
Comparison fields: 5 of 150
- Artificial Intelligence 4.4k
- Computer Vision and Pattern Recognition 2.1k
- Molecular Biology 535
- Signal Processing 512
- Information Systems 511
Countries citing papers authored by Ben Taskar
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | {Efficient Second-Order Gradient Boosting for Conditional Random Fields} | 12 |
| 2 | {PAC-Bayesian Collective Stability} | 7 |
| 3 | Nystrom Approximation for Large-Scale Determinantal Processes | 18 |
| 4 | Approximate Inference in Continuous Determinantal Processes | 21 |
| 5 | Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction | 9 |
| 6 | Near-Optimal MAP Inference for Determinantal Point Processes | 48 |
| 7 | Posterior Sparsity in Unsupervised Dependency Parsing | 26 |
| 8 | k-DPPs: Fixed-Size Determinantal Point Processes | 87 |
| 9 | 7 | |
| 10 | Stuctured Predictions Cascades | 1 |
| 11 | Structured Determinantal Point Processes | 62 |
| 12 | Posterior Regularization for Structured Latent Variable Models | 254 |
| 13 | Semi-Supervised Learning with Adversarially Missing Label Information | 4 |
| 14 | Expectation Maximization, Posterior Constraints, and Statistical Alignment | 1 |
| 15 | Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) breakdown → | 326 |
| 16 | 61 | |
| 17 | Structured Prediction via the Extragradient Method | 39 |
| 18 | Max-Margin Parsing | 144 |
| 19 | Learning on the test data: leveraging Unseen features | 16 |
| 20 | Max-Margin Markov Networks breakdown → | 755 |
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.