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- David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Myers Kadie
- Journal of Machine Learning Research
- 2000

We describe a graphical model for probabilistic relationships|an alternative to the Bayesian network|called a dependency network. The graph of a dependency network, unlike a Bayesian network, is potentially cyclic. The probability component of a dependency network, like a Bayesian network, is a set of conditional distributions, one for each node given its… (More)

- Christopher Meek
- UAI
- 1995

This paper presents correct algorithms for answering the following two questions; (i) Does there exist a causal explanation con sistent with a set of background knowledge which explains all of the observed indepen dence facts in a sample? (ii) Given that there is such a causal explanation what are the causal relationships common to every such

- Yi Yang, Wen-tau Yih, Christopher Meek
- EMNLP
- 2015

We describe the WIKIQA dataset, a new publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering. Most previous work on answer sentence selection focuses on a dataset created using the TREC-QA data, which includes editor-generated questions and candidate answer sentences selected by matching… (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)

Recently several researchers have investi gated techniques for using data to learn Bayesian networks containing compact rep resentations for the conditional probability distributions (CPDs) stored at each node. The majority of this work has concentrated on using decision-tree representations for the CPDs. In addition, researchers typi cally apply… (More)

- Igor V. Cadez, David Heckerman, Christopher Meek, Padhraic Smyth, Steven White
- Data Mining and Knowledge Discovery
- 2003

We present a new methodology for exploring and analyzing navigation patterns on a web site. The patterns that can be analyzed consist of sequences of URL categories traversed by users. In our approach, we first partition site users into clusters such that users with similar navigation paths through the site are placed into the same cluster. Then, for each… (More)

- David Maxwell Chickering, David Heckerman, Christopher Meek
- Journal of Machine Learning Research
- 2003

In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a scoring criterion that favors the simplest structure for which the model is able to represent the generative distribution exactly. Our results therefore hold whenever the learning… (More)

In this paper, we study the answer sentence selection problem for question answering. Unlike previous work, which primarily leverages syntactic analysis through dependency tree matching, we focus on improving the performance using models of lexical semantic resources. Experiments show that our systems can be consistently and significantly improved with rich… (More)

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- Christopher Meek
- UAI
- 1995

A completeness result for d-separation ap plied to discrete Bayesian networks is pre sented and it is shown that in a strong measure-theoretic sense almost all discrete distributions for a given network structure are faithful; i.e. the independence facts true of the distribution are all and only those en tailed by the network structure.