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- Pedro M. Domingos, Daniel Lowd
- Markov Logic: An Interface Layer for Artificial…
- 2009

markov logic an interface layer for artificial markov logic an interface layer for artificial shinichi tsukada in size 22 syyjdjbook.buncivy yumina ooba in size 24 ajfy7sbook.ztoroy okimi in size 15 edemembookkey.16mb markov logic an interface layer for artificial intelligent systems (ai-2) ubc computer science interface layer for artificial intelligence… (More)

- Daniel Lowd, Pedro M. Domingos
- PKDD
- 2007

Markov logic networks (MLNs) combine Markov networks and first-order logic, and are a powerful and increasingly popular representation for statistical relational learning. The state-of-the-art method for discriminative learning of MLN weights is the voted perceptron algorithm, which is essentially gradient descent with an MPE approximation to the expected… (More)

- Daniel Lowd, Christopher Meek
- KDD
- 2005

Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier [2]. In this paper, we introduce the adversarial classifier… (More)

- Pedro M. Domingos, Daniel Lowd, +4 authors Parag Singla
- LICS
- 2006

Intelligent agents must be able to handle the complexity and uncertainty of the real world. Logical AI has focused mainly on the former, and statistical AI on the latter. Markov logic combines the two by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on… (More)

- Daniel Lowd, Pedro M. Domingos
- ICML
- 2005

Naive Bayes models have been widely used for clustering and classification. However, they are seldom used for general probabilistic learning and inference (i.e., for estimating and computing arbitrary joint, conditional and marginal distributions). In this paper we show that, for a wide range of benchmark datasets, naive Bayes models learned using EM have… (More)

- Daniel Lowd, Christopher Meek
- CEAS
- 2005

Unsolicited commercial email is a significant problem for users and providers of email services. While statistical spam filters have proven useful, senders of spam are learning to bypass these filters by systematically modifying their email messages. In a good word attack, one of the most common techniques, a spammer modifies a spam message by inserting or… (More)

- Amirmohammad Rooshenas, Daniel Lowd
- ICML
- 2014

Sum-product networks (SPNs) are a deep probabilistic representation that allows for efficient, exact inference. SPNs generalize many other tractable models, including thin junction trees, latent tree models, and many types of mixtures. Previous work on learning SPN structure has mainly focused on using top-down or bottom-up clustering to find mixtures,… (More)

- Pedro M. Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, Parag Singla
- Probabilistic Inductive Logic Programming
- 2008

Most real-world machine learning problems have both statistical and relational aspects. Thus learners need representations that combine probability and relational logic. Markov logic accomplishes this by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Inference algorithms for Markov logic draw on… (More)

- Daniel Lowd, Amirmohammad Rooshenas
- AISTATS
- 2013

Markov networks are an effective way to represent complex probability distributions. However, learning their structure and parameters or using them to answer queries is typically intractable. One approach to making learning and inference tractable is to use approximations, such as pseudo-likelihood or approximate inference. An alternate approach is to use a… (More)

- Shangpu Jiang, Daniel Lowd, Dejing Dou
- 2012 IEEE 12th International Conference on Data…
- 2012

A number of text mining and information extraction projects such as Text Runner and NELL seek to automatically build knowledge bases from the rapidly growing amount of information on the web. In order to scale to the size of the web, these projects often employ ad hoc heuristics to reason about uncertain and contradictory information rather than reasoning… (More)