6,989 research outputs found
Learning Vine Copula Models For Synthetic Data Generation
A vine copula model is a flexible high-dimensional dependence model which
uses only bivariate building blocks. However, the number of possible
configurations of a vine copula grows exponentially as the number of variables
increases, making model selection a major challenge in development. In this
work, we formulate a vine structure learning problem with both vector and
reinforcement learning representation. We use neural network to find the
embeddings for the best possible vine model and generate a structure.
Throughout experiments on synthetic and real-world datasets, we show that our
proposed approach fits the data better in terms of log-likelihood. Moreover, we
demonstrate that the model is able to generate high-quality samples in a
variety of applications, making it a good candidate for synthetic data
generation
Recommended from our members
Mining learning preferences in web-based instruction: Holists vs. Serialists
Web-based instruction programs are used by learners with diverse knowledge, skills and needs. These differences determine their preferences for the design of Web-based instruction programs and ultimately influence learners' success in using them. Cognitive style has been found to significantly affect learners' preferences of web-based instruction programs. However, the majority of previous studies focus on Field Dependence/Independence. Pask's Holist/Serialist dimension has conceptual links with Field Dependence/Independence but it is left mostly unstudied. Therefore, this study focuses on identifying how this dimension of cognitive style affects learner preferences of Web-based instruction programs. A data mining approach is used to illustrate the difference in preferences between Holists and Serialists. The findings show that there are clear differences in regard to content presentation and navigation support. A set of design features were then produced to help designers incorporate cognitive styles into the development of Web-based instruction programs to ensure that they can accommodate learners' different preferences.This work is partially funded by National Science Council, Taiwan, ROC (NSC 98-2511-S-008-012- MY3; NSC 99-
2511-S-008 -003 -MY2; NSC 99-2631-S-008-001)
PAC-Bayesian Analysis of the Exploration-Exploitation Trade-off
We develop a coherent framework for integrative simultaneous analysis of the
exploration-exploitation and model order selection trade-offs. We improve over
our preceding results on the same subject (Seldin et al., 2011) by combining
PAC-Bayesian analysis with Bernstein-type inequality for martingales. Such a
combination is also of independent interest for studies of multiple
simultaneously evolving martingales.Comment: On-line Trading of Exploration and Exploitation 2 - ICML-2011
workshop. http://explo.cs.ucl.ac.uk/workshop
- …