Open AccessProceedings Article
Multi-Task Preference Learning with Gaussian Processes
Adriana Birlutiu,Perry Groot,Tom Heskes +2 more
- 01 Jan 2009
- pp 123-128
TL;DR: An EM-algorithm for the problem of learning user preferences with Gaussian processes in the context of multi-task learning is presented and predictive results for sound quality perception of normal hearing and hearingimpaired subjects can be improved using the hierarchical model.
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Abstract: We present an EM-algorithm for the problem of learning user preferences with Gaussian processes in the context of multi-task learning. We validate our approach on an audiological data set and show that predictive results for sound quality perception of normal hearing and hearingimpaired subjects, in the context of pairwise comparison experiments, can be improved using the hierarchical model.
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Citations
•Journal Article
Advances in computational intelligence and learning
92
Perception-based personalization of hearing aids using Gaussian processes and active learning
TL;DR: An interactive hearing-aid personalization system is proposed that obtains an optimal individual setting of the hearing aids from direct perceptual user feedback and may have potential for clinical usage to assist both the hearing-care professional and the user.
45
Multi-task preference learning with an application to hearing aid personalization
TL;DR: An EM-algorithm for the problem of learning preferences with semiparametric models derived from Gaussian processes in the context of multi-task learning is presented and predictive results for sound quality perception of hearing-impaired subjects can be improved using a hierarchical model.
43
Efficient individualization of hearing aid processed sound
Jens Nielsen,Jakob Blæsbjerg Nielsen +1 more
- 26 May 2013
TL;DR: An interactive system is proposed to ease and speed up fine tuning of hearing aids to suit the preference of the individual user while the system itself learns the user's preference.
5
Systems for Personalization of Hearing Instruments: A Machine Learning Approach
Jens Nielsen
- 01 Jan 2015
TL;DR: It is demonstrated that the learning rate of the novel paradigm is not only faster under ideal conditions, but also under adverse conditions where it seemingly preserves the robustness of the binary paradigm, suggesting that the new paradigm is robust to human inconsistency.
4
References
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Information Theory, Inference and Learning Algorithms
David J. C. MacKay
- 06 Oct 2003
TL;DR: A fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
•Dissertation
A family of algorithms for approximate bayesian inference
Tom Minka,Rosalind W. Picard +1 more
- 01 Jan 2001
TL;DR: This thesis presents an approximation technique that can perform Bayesian inference faster and more accurately than previously possible, and is found to be convincingly better than rival approximation techniques: Monte Carlo, Laplace's method, and variational Bayes.
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Task clustering and gating for bayesian multitask learning
Bart Bakker,Tom Heskes +1 more
TL;DR: A Bayesian approach is adopted in which some of the model parameters are shared and others more loosely connected through a joint prior distribution that can be learned from the data to combine the best parts of both the statistical multilevel approach and the neural network machinery.