Communications of the ACM
Negative Results for Equivalence Queries
Machine Learning
Lower Bound Methods and Separation Results for On-Line Learning Models
Machine Learning - Computational learning theory
Learning Conjunctions of Horn Clauses
Machine Learning - Computational learning theory
Exact learning Boolean functions via the monotone theory
Information and Computation
Asking questions to minimize errors
Journal of Computer and System Sciences
Machine Learning - Special issue on COLT '94
On the limits of proper learnability of subclasses of DNF formulas
Machine Learning - Special issue on COLT '94
Attribute-efficient learning in query and mistake-bound models
Journal of Computer and System Sciences
Learning Function-Free Horn Expressions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Learning closed horn expressions
Information and Computation
Machine Learning
Machine Learning
A Logic of Relative Desire (Preliminary Report)
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Unifying collaborative and content-based filtering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Exact learning of DNF formulas using DNF hypotheses
Journal of Computer and System Sciences - Special issue on COLT 2002
Hard and soft constraints for reasoning about qualitative conditional preferences
Journal of Heuristics
Graphically structured value-function compilation
Artificial Intelligence
On exploiting classification taxonomies in recommender systems
AI Communications - Recommender Systems
mCP nets: representing and reasoning with preferences of multiple agents
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Extending CP-nets with stronger conditional preference statements
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Artificial Intelligence Research
On graphical modeling of preference and importance
Journal of Artificial Intelligence Research
The computational complexity of dominance and consistency in CP-Nets
Journal of Artificial Intelligence Research
Generic preferences over subsets of structured objects
Journal of Artificial Intelligence Research
Preference-based configuration of web page content
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
The complexity of learning separable ceteris paribus preferences
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Ceteris Paribus preference elicitation with predictive guarantees
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Introducing variable importance tradeoffs into CP-nets
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
WCP-Nets: a weighted extension to CP-Nets for web service selection
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
Learning conditional preference network from noisy samples using hypothesis testing
Knowledge-Based Systems
An empirical investigation of ceteris paribus learnability
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Conditional preference networks (CP-nets) have recently emerged as a popular language capable of representing ordinal preference relations in a compact and structured manner. In this paper, we investigate the problem of learning CP-nets in the well-known model of exact identification with equivalence and membership queries. The goal is to identify a target preference ordering with a binary-valued CP-net by interacting with the user through a small number of queries. Each example supplied by the user or the learner is a preference statement on a pair of outcomes. In this model, we show that acyclic CP-nets are not learnable with equivalence queries alone, even if the examples are restricted to swaps for which dominance testing takes linear time. By contrast, acyclic CP-nets are what is called attribute-efficiently learnable when both equivalence queries and membership queries are available: we indeed provide a learning algorithm whose query complexity is linear in the description size of the target concept, but only logarithmic in the total number of attributes. Interestingly, similar properties are derived for tree-structured CP-nets in the presence of arbitrary examples. Our learning algorithms are shown to be quasi-optimal by deriving lower bounds on the VC-dimension of CP-nets. In a nutshell, our results reveal that active queries are required for efficiently learning CP-nets in large multi-attribute domains.