John Blitzer

12.9k citations
31 papers · 8.9k · 5 hit papers · h-index 20

Impact in

    • Domain Adaptation and Few-Shot Learning
    • Topic Modeling
    • Text and Document Classification Technologies
    • Sentiment Analysis and Opinion Mining
    • Natural Language Processing Techniques
    • Machine Learning and ELM
    • Advanced Text Analysis Techniques
    • Multimodal Machine Learning Applications

Papers in

    • Topic Modeling 18
    • Natural Language Processing Techniques 13
    • Text and Document Classification Technologies 6
    • Domain Adaptation and Few-Shot Learning 5
    • Machine Learning and Algorithms 4
    • Advanced Text Analysis Techniques 3
    • Multimodal Machine Learning Applications 3
Journals
Language Resources and Evaluation (1 paper)Machine Learning (1 paper)Political Analysis (1 paper)International Conference on Artificial Intelligence and Statistics (1 paper)Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University) (1 paper)

In The Last Decade

John Blitzer

31 papers receiving 8.5k citations

John Blitzer's Hit Papers

A theory of learning from different domains 2009 · 2.2k citations
2.2k0+7+14Years since publication50010001.5k2.0k

Peers

John Blitzer
Comparison fields: 5 of 163
  • Artificial Intelligence 6.9k
  • Computer Vision and Pattern Recognition 3.2k
  • Signal Processing 403
  • Media Technology 264
  • Information Systems 638
Replace Kurt Keutzer with:
Kurt Keutzer United States
Koby Crammer Israel
Fei Sha United States
Sam Gross Israel
Brian Kulis United States
Yuxin Wu China
Timothy M. Hospedales United Kingdom
Alex Kulesza United States
Yang Yang China
Pascal Germain Canada
John Blitzer relative to Kurt Keutzer United States Kurt Keutzer's profile →
Citations per field
00.5×1.7×
Kurt Keutzer · 1×
Citations per year

Countries citing papers authored by John Blitzer

Since Specialization
Citations

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

Fields of papers citing papers by John Blitzer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside John Blitzer, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with John Blitzer Line = papers co-authored together John Blitzer links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A theory of learning from different domains
Hit paper breakdown →
20092199
2
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
Hit paper breakdown →
20071504
3
Analysis of Representations for Domain Adaptation
Hit paper breakdown →
20071333
4
Distance Metric Learning for Large Margin Nearest Neighbor Classification
Hit paper breakdown →
20051325
5
Domain adaptation with structural correspondence learning
Hit paper breakdown →
20061120
6
Co-Training for Domain Adaptation
2011275
7 2004254
8
Learning Bounds for Domain Adaptation
2007241
9 2003105
10 200984
11
Domain Adaptation with Coupled Subspaces
201172
12
Frustratingly Hard Domain Adaptation for Dependency Parsing
200763
13 201040
14
Domain adaptation of natural language processing systems
200839
15
Regularized Learning with Networks of Features
200834
16
Learning Better Monolingual Models with Unannotated Bilingual Text
201031
17 200829
18
Hierarchical Distributed Representations for Statistical Language Modeling
200423
19 200322
20 201221

About John Blitzer

John Blitzer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Information Systems and Management and Sociology and Political Science, having authored 31 papers that have together received 8.9k indexed citations. Recurring topics across this work include Topic Modeling (18 papers), Natural Language Processing Techniques (13 papers), Text and Document Classification Technologies (6 papers), Domain Adaptation and Few-Shot Learning (5 papers), Machine Learning and Algorithms (4 papers), Personal Information Management and User Behavior (4 papers), Multimodal Machine Learning Applications (3 papers) and Advanced Text Analysis Techniques (3 papers). The work is most often cited by research in Artificial Intelligence (6.9k citations), Computer Vision and Pattern Recognition (3.2k citations), Signal Processing (403 citations), Media Technology (264 citations) and Information Systems (638 citations). John Blitzer has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Fernando Pereira, Koby Crammer, Shai Ben-David, Kilian Q. Weinberger, Mark Dredze, Lawrence K. Saul, Alex Kulesza, Ryan McDonald, Jennifer Wortman Vaughan and Minmin Chen. Their work appears in journals such as Language Resources and Evaluation, Machine Learning, Political Analysis, International Conference on Artificial Intelligence and Statistics and Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University).

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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