Stephen H. Bach

3.6k total citations · 2 hit papers
32 papers, 1.4k citations indexed

About

Stephen H. Bach is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research. According to data from OpenAlex, Stephen H. Bach has authored 32 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 5 papers in Management Science and Operations Research. Recurrent topics in Stephen H. Bach's work include Topic Modeling (8 papers), Machine Learning and Data Classification (8 papers) and Bayesian Modeling and Causal Inference (6 papers). Stephen H. Bach is often cited by papers focused on Topic Modeling (8 papers), Machine Learning and Data Classification (8 papers) and Bayesian Modeling and Causal Inference (6 papers). Stephen H. Bach collaborates with scholars based in United States, Belgium and Germany. Stephen H. Bach's co-authors include Alexander Ratner, Jure Leskovec, Himabindu Lakkaraju, Henry R. Ehrenberg, Jason Fries, Sen Wu, Christopher Ré, Lise Getoor, Marcus A. Maloof and Bert Huang and has published in prestigious journals such as Machine Learning, Proceedings of the VLDB Endowment and The VLDB Journal.

In The Last Decade

Stephen H. Bach

27 papers receiving 1.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Snorkel

2017 378 citations Alexander Ratner, Stephen H. Bach et al. profile →
Interpretable Decision Sets

2016 343 citations Stephen H. Bach et al. PubMed profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Stephen H. Bach United States	12	1.1k	203	162	138	100	32	1.4k
Zuhair Bandar United Kingdom	16	1.4k 1.3×	436 2.1×	106 0.7×	76 0.6×	179 1.8×	59	1.8k
Clare R. Voss United States	18	1.2k 1.1×	311 1.5×	145 0.9×	125 0.9×	105 1.1×	76	1.4k
Michihiro Yasunaga United States	14	1.3k 1.1×	259 1.3×	216 1.3×	109 0.8×	124 1.2×	21	1.5k
Paolo Torroni Italy	17	1.1k 1.0×	309 1.5×	110 0.7×	89 0.6×	33 0.3×	106	1.5k
Huan Sun United States	18	918 0.8×	371 1.8×	156 1.0×	131 0.9×	224 2.2×	67	1.3k
William Webber Australia	14	656 0.6×	615 3.0×	164 1.0×	154 1.1×	69 0.7×	35	1.2k
Liang Gou United States	18	709 0.6×	242 1.2×	435 2.7×	50 0.4×	59 0.6×	43	1.2k
Ting Liu China	20	1.6k 1.4×	256 1.3×	281 1.7×	87 0.6×	111 1.1×	95	2.0k
Ernesto William De Luca Germany	16	461 0.4×	327 1.6×	123 0.8×	69 0.5×	31 0.3×	80	949
Tyler Derr United States	18	820 0.7×	291 1.4×	176 1.1×	104 0.8×	67 0.7×	56	1.1k

Countries citing papers authored by Stephen H. Bach

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Stephen H. Bach

This figure shows the co-authorship network connecting the top 25 collaborators of Stephen H. Bach. A scholar is included among the top collaborators of Stephen H. Bach 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 Stephen H. Bach. Stephen H. Bach is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Nayak, Nihal V., et al.. (2025). K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction. 5–16.

2.

Li, Xiaochen, et al.. (2025). Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages. 11223–11240. 1 indexed citations

3.

Nayak, Nihal V., et al.. (2024). Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation. 12585–12611. 5 indexed citations

4.

Yong, Zheng, et al.. (2024). LexC-Gen: Generating Data for Extremely Low-Resource Languages with Large Language Models and Bilingual Lexicons. 13990–14009.

5.

Bach, Stephen H., et al.. (2024). Does CLIP Bind Concepts? Probing Compositionality in Large Image Models. 1487–1500.

6.

Bach, Stephen H., et al.. (2024). If CLIP Could Talk: Understanding Vision-Language Model Representations Through Their Preferred Concept Descriptions. 9797–9819.

7.

Zhang, Jieyu, et al.. (2023). Leveraging Large Language Models for Structure Learning in Prompted Weak Supervision. 29. 875–884. 1 indexed citations

8.

Yu, Peilin & Stephen H. Bach. (2023). Alfred: A System for Prompted Weak Supervision. 479–488. 1 indexed citations

9.

Park, Andrew, et al.. (2021). Semi-Supervised Aggregation of Dependent Weak Supervision Sources With Performance Guarantees. International Conference on Artificial Intelligence and Statistics. 3196–3204. 2 indexed citations

10.

Bach, Stephen H., et al.. (2021). Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees. 2021. 7534–7543. 5 indexed citations

11.

Ratner, Alexander, Stephen H. Bach, Henry R. Ehrenberg, et al.. (2019). Snorkel: rapid training data creation with weak supervision. The VLDB Journal. 29(2-3). 709–730. 201 indexed citations

12.

Ratner, Alexander, Stephen H. Bach, Henry R. Ehrenberg, et al.. (2017). Snorkel: A System for Lightweight Extraction.. Conference on Innovative Data Systems Research. 2 indexed citations

13.

Ratner, Alexander, Stephen H. Bach, Henry R. Ehrenberg, & Chris Ré. (2017). Snorkel. 1683–1686. 40 indexed citations

14.

Bach, Stephen H., Bryan He, Alexander Ratner, & Cristina Re. (2017). Learning the Structure of Generative Models without Labeled Data.. PubMed. 70. 273–82. 34 indexed citations

15.

Bach, Stephen H., Bert Huang, & Lise Getoor. (2015). Unifying Local Consistency and MAX SAT Relaxations for Scalable Inference with Rounding Guarantees. International Conference on Artificial Intelligence and Statistics. 46–55. 3 indexed citations

16.

Bach, Stephen H., Bert Huang, Ben London, & Lise Getoor. (2013). Hinge-loss Markov random fields: convex inference for structured prediction. arXiv (Cornell University). 32–41. 33 indexed citations

17.

Bach, Stephen H., Bert Huang, & Lise Getoor. (2013). Learning Latent Groups with Hinge-loss Markov Random Fields. 4 indexed citations

18.

Bach, Stephen H., Matthias Broecheler, Lise Getoor, & Dianne P. O’Leary. (2012). Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization. Neural Information Processing Systems. 25. 2654–2662. 24 indexed citations

19.

Bach, Stephen H., et al.. (2010). A Bayesian Approach to Concept Drift. Neural Information Processing Systems. 23. 127–135. 8 indexed citations

20.

Bach, Stephen H. & Marcus A. Maloof. (2008). Paired Learners for Concept Drift. 23–32. 87 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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