Abstract
Human preferences can effectively be elicited using pairwise
comparisons and in this paper current state-of-the-art based
on binary decisions is extended by a new paradigm which allows
subjects to convey their degree of preference as a continuous
but bounded response. For this purpose, a novel Betatype
likelihood is proposed and applied in a Bayesian regression
framework using Gaussian Process priors. Posterior estimation
and inference is performed using a Laplace approximation.
The potential of the paradigm is demonstrated and discussed
in terms of learning rates and robustness by evaluating
the predictive performance under various noise conditions on
a synthetic dataset. It is demonstrated that the learning rate
of the novel paradigm is not only faster under ideal conditions,
where continuous responses are naturally more informative
than binary decisions, 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.
Original language | English |
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Title of host publication | 2011 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) |
Publisher | IEEE |
Publication date | 2011 |
ISBN (Print) | 978-1-4577-1621-8 |
ISBN (Electronic) | 978-1-4577-1622-5 |
DOIs | |
Publication status | Published - 2011 |
Event | 2011 IEEE International Workshop on Machine Learning for Signal Processing - Beijing, China Duration: 18 Sept 2011 → 21 Sept 2011 Conference number: 21 https://ieeexplore.ieee.org/xpl/conhome/6058570/proceeding |
Conference
Conference | 2011 IEEE International Workshop on Machine Learning for Signal Processing |
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Number | 21 |
Country/Territory | China |
City | Beijing |
Period | 18/09/2011 → 21/09/2011 |
Internet address |
Series | Machine Learning for Signal Processing |
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ISSN | 1551-2541 |
Keywords
- Laplace Approximation
- Gaussian Processes
- Continuous Response
- Pairwise Comparisons