Hal Daumé

17.1k total citations · 6 hit papers
177 papers, 8.3k citations indexed

About

Hal Daumé is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Hal Daumé has authored 177 papers receiving a total of 8.3k indexed citations (citations by other indexed papers that have themselves been cited), including 149 papers in Artificial Intelligence, 33 papers in Computer Vision and Pattern Recognition and 17 papers in Information Systems. Recurrent topics in Hal Daumé's work include Topic Modeling (85 papers), Natural Language Processing Techniques (66 papers) and Machine Learning and Algorithms (18 papers). Hal Daumé is often cited by papers focused on Topic Modeling (85 papers), Natural Language Processing Techniques (66 papers) and Machine Learning and Algorithms (18 papers). Hal Daumé collaborates with scholars based in United States, United Kingdom and Russia. Hal Daumé's co-authors include Abhishek Kumar, Daniel Marcu, Piyush Rai, Jordan Boyd‐Graber, Mohit Iyyer, Lucy Vanderwende, Katrin Kirchhoff, Abhishek Sharma, Anurag Kumar and Amit Goyal and has published in prestigious journals such as The Journal of Chemical Physics, PLoS ONE and Proceedings of the IEEE.

In The Last Decade

Hal Daumé

167 papers receiving 7.9k citations

Hit Papers

5 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.

Co-regularized Multi-view Spectral Clustering

2011 691 citations Abhishek Kumar, Piyush Rai et al. Neural Information Processing Systems profile →
Generalized Multiview Analysis: A discriminative latent space

2012 539 citations Hal Daumé et al. profile →
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2013 517 citations Hal Daumé et al. profile →
Domain Adaptation for Statistical Classifiers

2006 516 citations Hal Daumé et al. profile →
A Co-training Approach for Multi-view Spectral Clustering

2011 485 citations Abhishek Kumar, Hal Daumé International Conference on Machine Learning profile →
Deep Unordered Composition Rivals Syntactic Methods for Text Classification

2015 448 citations Hal Daumé et al. profile →

Peers

Countries citing papers authored by Hal Daumé

Since Specialization

Citations

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

Fields of papers citing papers by Hal Daumé

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hal Daumé

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

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Exploring Collaboration to Center the Deaf Community in Sign Language AI 2025 ·PubMed ·(unknown), (unknown), Alex X. Lu, Hal Daumé, Hernisa Kacorri, Danielle Bragg	0
2	“You Gotta be a Doctor, Lin” : An Investigation of Name-Based Bias of Large Language Models in Employment Recommendations 2024 ·(unknown), John Prindle, Jieyu Zhao, Hal Daumé	3
3	Toxicity Detection is NOT all you Need: Measuring the Gaps to Supporting Volunteer Content Moderators through a User-Centric Method 2024 ·(unknown), (unknown), Sarah Gilbert, Michelle L. Mazurek, Katie Shilton, Hal Daumé	1
4	HateCOT: An Explanation-Enhanced Dataset for Generalizable Offensive Speech Detection via Large Language Models 2024 ·(unknown), Hal Daumé	2
5	Seamful XAI: Operationalizing Seamful Design in Explainable AI 2024 ·Proceedings of the ACM on Human-Computer Interaction ·Upol Ehsan, Q. Vera Liao, Samir Passi, Mark Riedl, Hal Daumé	5
6	The Impact of Explanations on Fairness in Human-AI Decision-Making: Protected vs Proxy Features 2024 ·(unknown), (unknown), (unknown), Hal Daumé	4
7	A Rose by Any Other Name would not Smell as Sweet: Social Bias in Names Mistranslation 2023 ·(unknown), Jieyu Zhao, Marine Carpuat, Hal Daumé	2
8	Towards Conceptualization of “Fair Explanation”: Disparate Impacts of anti-Asian Hate Speech Explanations on Content Moderators 2023 ·(unknown), (unknown), (unknown), Hal Daumé, Marine Carpuat	0
9	Human-Centered Explainable AI (HCXAI): Coming of Age 2023 ·Upol Ehsan, Philipp Wintersberger, Elizabeth Anne Watkins, (unknown), Gonzalo Ramos, Justin D. Weisz, Hal Daumé, Andreas Riener, Mark Riedl	14
10	Understanding MOOC Discussion Forums using Seeded LDA 2014 ·Arti Ramesh, Dan Goldwasser, Bert Huang, Hal Daumé, Lise Getoor	65
11	Simultaneously Leveraging Output and Task Structures for Multiple-Output Regression 2012 ·Neural Information Processing Systems ·Piyush Rai, Abhishek Kumar, Hal Daumé	40
12	Imitation Learning by Coaching 2012 ·Neural Information Processing Systems ·He He, Jason Eisner, Hal Daumé	46
13	A Co-training Approach for Multi-view Spectral Clustering breakdown → 2011 ·International Conference on Machine Learning ·Abhishek Kumar, Hal Daumé	485
14	A corpus-guided framework for robotic visual perception 2011 ·National Conference on Artificial Intelligence ·Ching L. Teo, Yezhou Yang, Hal Daumé, Cornelia Fermüller, Yiannis Aloimonos	6
15	Co-regularized Multi-view Spectral Clustering breakdown → 2011 ·Neural Information Processing Systems ·Abhishek Kumar, Piyush Rai, Hal Daumé	691
16	Improving Bilingual Projections via Sparse Covariance Matrices 2011 ·Empirical Methods in Natural Language Processing ·Jagadeesh Jagarlamudi, Raghavendra Udupa, Hal Daumé, (unknown)	10
17	Corpus-Guided Sentence Generation of Natural Images 2011 ·Empirical Methods in Natural Language Processing ·Yezhou Yang, Ching L. Teo, Hal Daumé, Yiannis Aloimonos	220
18	Message-Passing for Approximate MAP Inference with Latent Variables 2011 ·Neural Information Processing Systems ·(unknown), Piyush Rai, Hal Daumé	9
19	Co-regularization Based Semi-supervised Domain Adaptation 2010 ·Neural Information Processing Systems ·Abhishek Kumar, Avishek Saha, Hal Daumé	99
20	Infinite Predictor Subspace Models for Multitask Learning 2010 ·International Conference on Artificial Intelligence and Statistics ·Piyush Rai, Hal Daumé	47

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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