John Lafferty
Impact in
- Artificial Intelligence top 0.01%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
-
- Advanced Image and Video Retrieval Techniques
- Image Retrieval and Classification Techniques
Papers in
-
- Topic Modeling 26
- Natural Language Processing Techniques 23
- Bayesian Modeling and Causal Inference 20
- Bayesian Methods and Mixture Models 19
- Machine Learning and Algorithms 14
- Algorithms and Data Compression 13
-
- Statistical Methods and Inference 25
- Co-authors
- Andrew McCallum (3 shared papers)Fernando C. N. Pereira (1 shared paper)ChengXiang Zhai (10 shared papers)David M. Blei (6 shared papers)Xiaojin Zhu (11 shared papers)Zoubin Ghahramani (4 shared papers)Adam Berger (9 shared papers)V. Della Pietra (2 shared papers)
- Journals
- American Journal of Clinical Pathology (8 papers)ACM SIGIR Forum (5 papers)Transactions of the American Mathematical Society (4 papers)Journal of Machine Learning Research (4 papers)Archives of Pathology & Laboratory Medicine (3 papers)
- Partner nations
- United StatesCanadaUnited Kingdom
In The Last Decade
John Lafferty
173 papers receiving 23.9k citations
John Lafferty's Hit Papers
Peers
Comparison fields: 5 of 218
- Artificial Intelligence 17.5k
- Computer Vision and Pattern Recognition 5.9k
- Information Systems 5.3k
- Signal Processing 2.1k
- General Social Sciences 627
Countries citing papers authored by John Lafferty
This map shows the geographic impact of John Lafferty'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 John Lafferty with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Lafferty more than expected).
Fields of papers citing papers by John Lafferty
This network shows the impact of papers produced by John Lafferty. 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 John Lafferty. The network helps show where John Lafferty may publish in the future.
Co-authors
The 25 scholars most cited alongside John Lafferty, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 178 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data Hit paper breakdown → | 2001 | 8291 |
| 2 | Semi-supervised learning using Gaussian fields and harmonic functions Hit paper breakdown → | 2003 | 2312 |
| 3 | Dynamic topic models Hit paper breakdown → | 2006 | 1502 |
| 4 | A statistical approach to machine translation Hit paper breakdown → | 1990 | 1013 |
| 5 | A study of smoothing methods for language models applied to information retrieval Hit paper breakdown → | 2004 | 776 |
| 6 | Inducing features of random fields Hit paper breakdown → | 1997 | 709 |
| 7 | Correlated Topic Models Hit paper breakdown → | 2005 | 611 |
| 8 | A study of smoothing methods for language models applied to Ad Hoc information retrieval Hit paper breakdown → | 2001 | 610 |
| 9 | Using Maximum Entropy for Text Classification Hit paper breakdown → | 1999 | 522 |
| 10 | 2001 | 497 | |
| 11 | Diffusion Kernels on Graphs and Other Discrete Input Spaces Hit paper breakdown → | 2002 | 462 |
| 12 | Semi-supervised learning with graphs Hit paper breakdown → | 2005 | 434 |
| 13 | 1999 | 434 | |
| 14 | A correlated topic model of Science Hit paper breakdown → | 2018 | 386 |
| 15 | 2001 | 365 | |
| 16 | Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions | 2003 | 317 |
| 17 | 2004 | 309 | |
| 18 | 2009 | 305 | |
| 19 | 2012 | 277 | |
| 20 | Expectation-propagation for the generative aspect model | 2002 | 266 |
About John Lafferty
John Lafferty is a scholar working on Artificial Intelligence, Statistics and Probability, Information Systems, Genetics and Signal Processing, having authored 178 papers that have together received 26.1k indexed citations. Recurring topics across this work include Topic Modeling (26 papers), Statistical Methods and Inference (25 papers), Natural Language Processing Techniques (23 papers), Bayesian Modeling and Causal Inference (20 papers), Bayesian Methods and Mixture Models (19 papers), Hemoglobinopathies and Related Disorders (18 papers), Machine Learning and Algorithms (14 papers) and Algorithms and Data Compression (13 papers). The work is most often cited by research in Artificial Intelligence (17.5k citations), Computer Vision and Pattern Recognition (5.9k citations), Information Systems (5.3k citations), Signal Processing (2.1k citations) and General Social Sciences (627 citations). John Lafferty has collaborated with scholars based in United States, Canada and United Kingdom. Frequent co-authors include Andrew McCallum, Fernando C. N. Pereira, ChengXiang Zhai, David M. Blei, Xiaojin Zhu, Zoubin Ghahramani, Adam Berger, V. Della Pietra, S. Della Pietra and Risi Kondor. Their work appears in journals such as American Journal of Clinical Pathology, ACM SIGIR Forum, Transactions of the American Mathematical Society, Journal of Machine Learning Research and Archives of Pathology & Laboratory Medicine.
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.