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Learning from Sparse Data by Exploiting Monotonicity Constraints
TLDR
We show how to interpret knowledge of qualitative influences, and in particular of monotonicities, as constraints on probability distributions, and to incorporate this knowledge into Bayesian network learning algorithms. Expand
Learning first-order probabilistic models with combining rules
TLDR
This paper presents algorithms for learning with combining rules in first-order relational probabilistic models. Expand
Inferring appropriate eligibility criteria in clinical trial protocols without labeled data
TLDR
We consider the user task of designing clinical trial protocols and propose a method that outputs the most appropriate eligibility criteria from a potentially huge set of candidates. Expand
Preference Elicitation via Theory Refinement
TLDR
We present an approach to elicitation of user preference models in which assumptions can be used to guide but not constrain the elicitation process. Expand
Detecting experimental techniques and selecting relevant documents for protein-protein interactions from biomedical literature
TLDR
We proposed and compared several classification-based methods for both tasks, employing rich contextual features as well as features extracted from external knowledge sources. Expand
Text mining for efficient search and assisted creation of clinical trials
Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query isExpand
ARGUER: using argument schemas for argument detection and rebuttal in dialogs
TLDR
This paper presents a computational method for argumentation on the basis of a declarative characterization of the structure of arguments. Expand
Building intelligent dialog systems
TLDR
We overview our recent work in specifying and building intelligent dialog systems that collaborate with users for a task using rich models of dialog for human-computer communication. Expand
A method for discovering and inferring appropriate eligibility criteria in clinical trial protocols without labeled data
TLDR
We have proposed LDALR, a practical method for discovering and inferring appropriate eligibility criteria in clinical trial protocols without labeled data. Expand
Eliciting Utilities by Refining Theories of Monotonicity and Risk
TLDR
We use knowledge-based artificial neural networks to encode assumptions about a decision maker’ s preferences and show that learning speed and accuracy increase as more domain knowledge is included in the neural net. Expand
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