Rank Pruning for Dominance Queries in CP-Nets
Conditional preference networks (CP-nets) are a graphical representation of a person's (conditional) preferences over a set of discrete variables. In this paper, we introduce a novel method of quantifying preference for any given outcome based on a CP-net representation of a user's preferences. We demonstrate that these values are useful for reasoning about user preferences. In particular, they allow us to order (any subset of) the possible outcomes in accordance with the user's preferences. Further, these values can be used to improve the efficiency of outcome dominance testing. That is, given a pair of outcomes, we can determine which the user prefers more efficiently. Through experimental results, we show that this method is more effective than existing techniques for improving dominance testing efficiency. We show that the above results also hold for CP-nets that express indifference between variable values.
Code (1)
Similar Papers 제목 키워드 기반
The Computational Complexity of Dominance and Consistency in CP-Nets
We investigate the computational complexity of testing dominance and consistency in CP-nets. Previously, the complexity of dominance has been determined for restricted classes in which the dependency graph of the CP-net …
âLearning-Compressionâ Algorithms for Neural Net Pruning
Pruning a neural net consists of removing weights without degrading its performance. This is an old problem of renewed interest because of the need to compress ever larger nets so they can run in mobile devices. Pruning …
Model CompressionNetwork PruningTowards Efficient Model Compression via Learned Global Ranking
Pruning convolutional filters has demonstrated its effectiveness in compressing ConvNets. Prior art in filter pruning requires users to specify a target model complexity (e.g., model size or FLOP count) for the resulting…
Model CompressionAn extended Knowledge Compilation Map for Conditional Preference Statements-based and Generalized Additive Utilities-based Languages
Conditional preference statements have been used to compactly represent preferences over combinatorial domains. They are at the core of CP-nets and their generalizations, and lexicographic preference trees. Several works…
PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator
Channel Pruning is one of the most widespread techniques used to compress deep neural networks while maintaining their performances. Currently, a typical pruning algorithm leverages neural architecture search to directly…
Neural Architecture Search