Stephen H. Bach
Impact in
- Artificial Intelligence top 1%
- Topic Modeling
- Machine Learning and Data Classification
- Explainable Artificial Intelligence (XAI)
- Data Stream Mining Techniques
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
- Health Informatics top 5%
Papers in
-
- Topic Modeling 8
- Machine Learning and Data Classification 8
- Bayesian Modeling and Causal Inference 6
- Natural Language Processing Techniques 5
- Domain Adaptation and Few-Shot Learning 4
- Data Stream Mining Techniques 3
- Explainable Artificial Intelligence (XAI) 3
-
- Multimodal Machine Learning Applications 3
- Co-authors
- Alexander Ratner (5 shared papers)Jure Leskovec (1 shared paper)Himabindu Lakkaraju (1 shared paper)Henry R. Ehrenberg (4 shared papers)Jason Fries (4 shared papers)Sen Wu (3 shared papers)Christopher Ré (1 shared paper)Marcus A. Maloof (1 shared paper)
- Journals
- The VLDB Journal (1 paper)Machine Learning (1 paper)Proceedings of the VLDB Endowment (1 paper)PubMed (3 papers)Lirias (KU Leuven) (2 papers)
- Partner nations
- United StatesBelgiumGermany
In The Last Decade
Stephen H. Bach
29 papers receiving 1.3k citations
Stephen H. Bach's Hit Papers
Peers
Comparison fields: 5 of 105
- Artificial Intelligence 1.1k
- Health Informatics 36
- Management Science and Operations Research 139
- Computer Science Applications 60
- Information Systems 204
Countries citing papers authored by Stephen H. Bach
This map shows the geographic impact of Stephen H. Bach'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 Stephen H. Bach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen H. Bach more than expected).
Fields of papers citing papers by Stephen H. Bach
This network shows the impact of papers produced by Stephen H. Bach. 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 Stephen H. Bach. The network helps show where Stephen H. Bach may publish in the future.
Co-authors
The 25 scholars most cited alongside Stephen H. Bach, 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 33 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Snorkel Hit paper breakdown → | 2017 | 390 |
| 2 | Interpretable Decision Sets Hit paper breakdown → | 2016 | 354 |
| 3 | 2019 | 208 | |
| 4 | A short introduction to probabilistic soft logic | 2012 | 95 |
| 5 | 2008 | 87 | |
| 6 | 2019 | 46 | |
| 7 | 2017 | 42 | |
| 8 | 2020 | 38 | |
| 9 | Learning the Structure of Generative Models without Labeled Data. | 2017 | 35 |
| 10 | Hinge-loss Markov random fields: convex inference for structured prediction | 2013 | 33 |
| 11 | Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization | 2012 | 24 |
| 12 | Social Group Modeling with Probabilistic Soft Logic | 2012 | 15 |
| 13 | 2017 | 10 | |
| 14 | 2013 | 9 | |
| 15 | A Bayesian Approach to Concept Drift | 2010 | 8 |
| 16 | 2024 | 5 | |
| 17 | 2024 | 5 | |
| 18 | Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees | 2021 | 5 |
| 19 | Paired-Dual Learning for Fast Training of Latent Variable Hinge-Loss MRFs | 2015 | 5 |
| 20 | Learning Latent Groups with Hinge-loss Markov Random Fields | 2013 | 4 |
About Stephen H. Bach
Stephen H. Bach is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Information Systems and Computer Science Applications, having authored 33 papers that have together received 1.4k indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Machine Learning and Data Classification (8 papers), Bayesian Modeling and Causal Inference (6 papers), Natural Language Processing Techniques (5 papers), Domain Adaptation and Few-Shot Learning (4 papers), Multimodal Machine Learning Applications (3 papers), Data Stream Mining Techniques (3 papers) and Explainable Artificial Intelligence (XAI) (3 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Health Informatics (36 citations), Management Science and Operations Research (139 citations), Computer Science Applications (60 citations) and Information Systems (204 citations). Stephen H. Bach has collaborated with scholars based in United States, Belgium and Germany. Frequent co-authors include Alexander Ratner, Jure Leskovec, Himabindu Lakkaraju, Henry R. Ehrenberg, Jason Fries, Sen Wu, Christopher Ré, Marcus A. Maloof, Lise Getoor and Bert Huang. Their work appears in journals such as The VLDB Journal, Machine Learning, Proceedings of the VLDB Endowment, PubMed and Lirias (KU Leuven).
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.