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Publications

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Books

• Markov Logic: An Interface Layer for Artificial Intelligence. Pedro Domingos and Daniel Lowd. San Rafael, CA: Morgan & Claypool, 2009.

Book Chapters

• Markov Logic: A Language and Algorithms for Link Mining. Pedro Domingos, Daniel Lowd, Stanley Kok, Hoifung Poon, Matthew Richardson, and Parag Singla. P. Yu, C. Faloutsos, and J. Han (eds.), Link Mining: Models, Algorithms and Applications, 2010. New York: Springer.
• Just Add Weights: Markov Logic for the Semantic Web. Pedro Domingos, Daniel Lowd, Stanley Kok, Hoifung Poon, Matthew Richardson, and Parag Singla. In P. C. G. Costa, C. d'Amato, N. Fanizzi, K. B. Laskey, K. J. Laskey, T. Lukasiewicz, M. Nickles, and M. Pool (eds.), Uncertain Reasoning for the Semantic Web I, 2008. New York: Springer.
• Markov Logic. Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, and Parag Singla. In L. De Raedt, P. Frasconi, K. Kersting and S. Muggleton (eds.), Probabilistic Inductive Logic Programming (pp. 92-117), 2008. New York: Springer.

Journal Papers

• Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees. Jonathan Brophy, Zayd Hammoudeh, and Daniel Lowd. JMLR 2023.
• Unifying Logical and Statistical AI with Markov Logic. Pedro Domingos and Daniel Lowd. Communications of the ACM, vol.62, no.7, July 2019.
• The Libra Toolkit for Probabilistic Models. Daniel Lowd and Amirmohammad Rooshenas. Journal of Machine Learning Research, vol.16, 2015.
• Improving Markov Network Structure Learning Using Decision Trees. Daniel Lowd and Jesse Davis. Journal of Machine Learning Research, vol.15, 2014.

Selected Refereed Conference Papers

• Reducing Certified Regression to Certified Classification for General Poisoning Attacks. Zayd Hammoudeh and Daniel Lowd. SaTML 2023.
• Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees. Jonathan Brophy and Daniel Lowd. NeurIPS 2022.
• Identifying a Training-Set Attack's Target Using Renormalized Influence Estimation. Zayd Hammoudeh and Daniel Lowd. CCS 2022.
• Machine unlearning for random forests. Jonathan Brophy and Daniel Lowd. ICML 2021. (Supplement).
• Learning from Positive and Unlabeled Data with Arbitrary Positive Shift. Zayd Hammoudeh and Daniel Lowd. In Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Virtual, 2020. (Source code)
• On the Practicality of Learning Models for Network Telemetry. Soheil Jamshidi, Zayd Hammoudeh, Ramakrishnan Durairajan, Daniel Lowd, Reza Rejaie, and Walter Willinger. In Proceedings of the Network Traffic Measurement and Analysis Conference (TMA 2020), Berlin, Germany, 2020.
• On Adversarial Examples for Character-Level Neural Machine Translation. Javid Ebrahimi, Daniel Lowd, and Dejing Dou. In Proceedings of the 27th International Conference on Computational Linguistics (COLING 2018), Santa Fe, New Mexico, USA, 2018. (Source code)
• HotFlip: White-Box Adversarial Examples for Text Classification. Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018) (short paper), Melbourne, Australia, 2018.
• A Temporal Attentional Model for Rumor Stance Classification. A. Pouran Ben Veyseh, Javid Ebrahimi, Dejing Dou, and Daniel Lowd. In Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM) (short paper), Singapore, 2017.
• A Joint Sentiment-Target-Stance Model for Stance Classification in Tweets Javid Ebrahimi, Dejing Dou, and Daniel Lowd. Proceedings of the 26th International Conference on Computational Linguistics (COLING 2016) (pp. 2656-2665), 2016.
• Weakly Supervised Tweet Stance Classification by Relational Bootstrapping. Javid Ebrahimi, Dejing Dou, and Daniel Lowd. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2016) (pp. 1012-1017) (short paper), 2016.
• Ontology-based Deep Restricted Boltzmann Machine. Hao Wang, Dejing Dou, and Daniel Lowd. Proceedings of the 27th International Conference on Database and Expert Systems Applications (DEXA 2016) (pp. 431-445), 2016. Porto, Portugal.
• Discriminative Structure Learning of Arithmetic Circuits. Amirmohammad Rooshenas and Daniel Lowd. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. Cadiz, Spain. (Supplementary material)
• A Probabilistic Approach to Knowledge Translation. Shangpu Jiang, Daniel Lowd, and Dejing Dou. Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), 2016. Phoenix, AZ.
• Ontology Matching with Knowledge Rules. Shangpu Jiang, Daniel Lowd, and Dejing Dou. Proceedings of the 26th International Conference on Database and Expert Systems Applications (DEXA), 2015. Valencia, Spain. (Best Paper Award)
• Inferring Coarse Views of Connectivity in Very Large Graphs. Reza Motamedi, Reza Rejaie, Walter Willinger, Daniel Lowd, and Roberto Gonzalez. Proceedings of the ACM Conference on Online Social Networks (COSN'14), 2014. Dublin, Ireland.
• On Robustness and Regularization of Structural Support Vector Machines. MohamadAli Torkamani and Daniel Lowd. Proceedings of the 31st International Conference on Machine Learning (ICML), 2014. Beijing, China. (Supplemental proofs)
• Learning Sum-Product Networks with Direct and Indirect Interactions. Amirmohammad Rooshenas and Daniel Lowd. Proceedings of the 31st International Conference on Machine Learning (ICML), 2014. Beijing, China.
• Leveraging USB to Establish Host Identity Using Commodity Devices. Adam Bates, Ryan Leonard, Hannah Pruse, Daniel Lowd, and Kevin Butler. Proceedings of the 21st ISOC Network and Distributed System Security Symposium (NDSS), 2014. San Diego, CA.
• Learning Markov Networks with Arithmetic Circuits. Daniel Lowd and Amirmohammad Rooshenas. Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS), 2013. Scottsdale, AZ, USA.
• Convex Adversarial Collective Classification. MohamadAli Torkamani and Daniel Lowd. Proceedings of the 30th International Conference on Machine Learning (ICML), 2013. Atlanta, GA, USA.
• Learning to Refine an Automatically Extracted Knowledge Base using Markov Logic. Shangpu Jiang, Daniel Lowd, and Dejing Dou. Proceedings of the IEEE International Conference on Data Mining (ICDM), 2012. Brussels, Belgium. (Workshop version; Data)
• Closed-Form Learning of Markov Networks from Dependency Networks. Daniel Lowd. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI-12), 2012. Catalina Island, CA. (Spotlight) (Poster)
• Mean Field Inference in Dependency Networks: An Empirical Study. Daniel Lowd and Arash Shamaei. Proceedings of the 25th Conference on Artificial Intelligence (AAAI-11), 2011. San Francisco, CA. (Slides) (Online appendix)
• Approximate Inference by Compilation to Arithmetic Circuits. Daniel Lowd and Pedro Domingos. Advances in Neural Information Processing Systems (NIPS) 23, 2010. Vancouver, BC. (Supplemental proof and text.)
• Learning Markov Network Structure with Decision Trees. Daniel Lowd and Jesse Davis. Proceedings of the 10th IEEE International Conference on Data Mining (ICDM), 2010. Sydney, Australia. (Slides) (Source code)
• Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. Sriraam Natarajan, Tushar Khot, Daniel Lowd, Kristian Kersting, Prasad Tadepalli and Jude Shavlik. European Conference on Machine Learning (ECML), 2010.
• Using Salience to Segment Desktop Activity into Projects. Daniel Lowd and Nicholas Kushmerick. Proceedings of the Thirteenth International Conference on Intelligent User Interfaces (IUI), 2009. Sanibel Island, Florida: ACM Press. (Poster)
• Learning Arithmetic Circuits. Daniel Lowd and Pedro Domingos. Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI), 2008. Helsinki, Finland: AUAI Press. (Extended version with proofs) (Poster) (Slides)
• Efficient Weight Learning for Markov Logic Networks. Daniel Lowd and Pedro Domingos. Proceedings of the Eleventh European Conference on Principles and Practices of Knowledge Discovery in Databases (PKDD), 2007. Warsaw, Poland: Springer Verlag. (Slides) (Video)
  [Updated PDF file fixes several formula errors.]
• Recursive Random Fields. Daniel Lowd and Pedro Domingos. Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI), 2007. Hyderabad, India: IJCAI. (Slides) (Slides+Audio)
• Naive Bayes Models for Probability Estimation. Daniel Lowd and Pedro Domingos. Proceedings of the Twenty-Second International Conference on Machine Learning (ICML), 2005. Bonn, Germany: ACM Press. (Slides) (Online appendix)
• Good Word Attacks on Statistical Spam Filters. Daniel Lowd and Christopher Meek. Second Conference on Email and Anti-Spam (CEAS), 2005. Palo Alto, CA. (Slides)
• Adversarial Learning. Daniel Lowd and Christopher Meek. Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2005. Chicago, IL: ACM Press. (Poster)

Selected Workshop Papers

• Collective Classification of Social Network Spam. Jonathan Brophy and Daniel Lowd. 2017 AAAI Workshop on Artificial Intelligence and Cyber-Security (AICS), 2017. San Francisco, CA: AAAI.
• Automated Attacks on Compression-Based Classifiers. Igor Burago and Daniel Lowd. 2015 ACM Workshop on Artificial Intelligence and Security (AISec), 2015. Denver, Colorado: ACM.
• A Probabilistic Approach to Knowledge Translation. Shangpu Jiang, Daniel Lowd, and Dejing Dou, Fifth International Workshop on Statistical Relational AI (StaR AI 2015), 2015.
• Ontology Matching with Knowledge Rules. Shangpu Jiang, Daniel Lowd, and Dejing Dou, Fifth International Workshop on Statistical Relational AI (StaR AI 2015), 2015.
• Towards Adversarial Reasoning in Statistical Relational Domains. Daniel Lowd, Brenton Lessley, and Mino De Raj. AAAI-14 Workshop on Statistical Relational AI (Star AI 2014), 2014.
• On the Hardness of Evading Combinations of Linear Classifiers. David Stevens and Daniel Lowd. 2013 ACM Workshop on Artificial Intelligence and Security (AISec), 2013. Berlin, Germany: ACM.
• Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models (workshop version). Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, and Jude Shavlik. AAAI-10 Workshop on Statistical Relational AI (Star AI 2010), 2010
• Recursive Random Fields (workshop version). Daniel Lowd and Pedro Domingos. Proceedings of the ICML-2006 Workshop on Open Problem in Statistical Relational Learning, 2006. Pittsburgh, PA: IMLS. (Slides)

Technical reports

• Challenges and Solutions for Synthesis of Knowledge Regarding Collaborative Filtering Algorithms. Daniel Lowd, Olivier Godde, Matthew McLaughlin, Shuzhen Nong, Yun Wang, and Jonathan L. Herlocker. Oregon State University, School of Electrical Engineering and Computer Science. 2009 (written in 2004). URI: http://hdl.handle.net/1957/10940