Publications
Books
Book chapters
- 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.
Refereed conference papers
- 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)
Workshop papers
Technical reports