Daniel Lowd

4.4k total citations · 2 hit papers
42 papers, 2.0k citations indexed

About

Daniel Lowd is a scholar working on Artificial Intelligence, Information Systems and Computer Networks and Communications. According to data from OpenAlex, Daniel Lowd has authored 42 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 7 papers in Information Systems and 6 papers in Computer Networks and Communications. Recurrent topics in Daniel Lowd's work include Bayesian Modeling and Causal Inference (17 papers), Machine Learning and Algorithms (11 papers) and Adversarial Robustness in Machine Learning (10 papers). Daniel Lowd is often cited by papers focused on Bayesian Modeling and Causal Inference (17 papers), Machine Learning and Algorithms (11 papers) and Adversarial Robustness in Machine Learning (10 papers). Daniel Lowd collaborates with scholars based in United States, Belgium and Germany. Daniel Lowd's co-authors include Pedro Domingos, Christopher Meek, Javid Ebrahimi, Dejing Dou, Anyi Rao, Dejing Dou, Jesse Davis, Stanley Kok, Parag Singla and Hoifung Poon and has published in prestigious journals such as Communications of the ACM, Machine Learning and Journal of Machine Learning Research.

In The Last Decade

Daniel Lowd

41 papers receiving 1.9k 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.

HotFlip: White-Box Adversarial Examples for Text Classification

2018 477 citations Javid Ebrahimi, Anyi Rao et al. profile →
Adversarial learning

2005 402 citations Daniel Lowd, Christopher Meek profile →

Peers

Countries citing papers authored by Daniel Lowd

Since Specialization

Citations

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

Fields of papers citing papers by Daniel Lowd

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Lowd

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Lowd. A scholar is included among the top collaborators of Daniel Lowd 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 Daniel Lowd. Daniel Lowd 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	Training data influence analysis and estimation: a survey 2024 ·Machine Learning ·(unknown), Daniel Lowd	15
2	Provable Robustness against a Union of L_0 Adversarial Attacks 2024 ·Proceedings of the AAAI Conference on Artificial Intelligence ·(unknown), Daniel Lowd	4
3	Reducing Certified Regression to Certified Classification for General Poisoning Attacks 2023 ·(unknown), Daniel Lowd	2
4	Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers 2023 ·(unknown), (unknown), Daniel Lowd	7
5	On the Practicality of Learning Models for Network Telemetry. 2020 ·(unknown), (unknown), Ramakrishnan Durairajan, Daniel Lowd, Reza Rejaie, Walter Willinger	2
6	HotFlip: White-Box Adversarial Examples for Text Classification breakdown → 2018 ·Javid Ebrahimi, Anyi Rao, Daniel Lowd, Dejing Dou	477
7	HotFlip: White-Box Adversarial Examples for NLP 2017 ·arXiv (Cornell University) ·Javid Ebrahimi, Anyi Rao, Daniel Lowd, Dejing Dou	19
8	A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets 2016 ·International Conference on Computational Linguistics ·Javid Ebrahimi, Dejing Dou, Daniel Lowd	20
9	Discriminative Structure Learning of Arithmetic Circuits 2016 ·Proceedings of the AAAI Conference on Artificial Intelligence ·(unknown), Daniel Lowd	3
10	On Robustness and Regularization of Structural Support Vector Machines 2014 ·International Conference on Machine Learning ·(unknown), Daniel Lowd	6
11	Learning Sum-Product Networks with Direct and Indirect Variable Interactions 2014 ·International Conference on Machine Learning ·(unknown), Daniel Lowd	49
12	Improving Markov network structure learning using decision trees 2014 ·Journal of Machine Learning Research ·Daniel Lowd, Jesse Davis	18
13	Learning tractable graphical models using mixture of arithmetic circuits 2013 ·National Conference on Artificial Intelligence ·(unknown), Daniel Lowd	2
14	Learning Markov Networks With Arithmetic Circuits 2013 ·International Conference on Artificial Intelligence and Statistics ·Daniel Lowd, (unknown)	27
15	Convex Adversarial Collective Classification 2013 ·International Conference on Machine Learning ·(unknown), Daniel Lowd	15
16	Approximate Inference by Compilation to Arithmetic Circuits 2010 ·Neural Information Processing Systems ·Daniel Lowd, Pedro Domingos	5
17	Markov Logic 2009 ·Pedro Domingos, Daniel Lowd	53
18	Markov logic 2008 ·Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla	42
19	Recursive random fields 2007 ·International Joint Conference on Artificial Intelligence ·Daniel Lowd, Pedro Domingos	10
20	Good Word Attacks on Statistical Spam Filters. 2005 ·Daniel Lowd, Christopher Meek	150

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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