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Thurstonian Boltzmann Machines: Learning from Multiple Inequalities [article]

Truyen Tran, Dinh Phung, Svetha Venkatesh
2014 arXiv   pre-print
We introduce Thurstonian Boltzmann Machines (TBM), a unified architecture that can naturally incorporate a wide range of data inputs at the same time.  ...  Thus learning and inference in TBM reduce to making sense of a set of inequalities.  ...  Thurstonian Boltzmann Machines We now generalise the Gaussian RBM into the Thurstonian Boltzmann Machine (TBM). Denote by e an observed evidence of x.  ... 
arXiv:1408.0055v1 fatcat:4y3vqsvwlrb4jjtoovk7yxxcc4

Estimation of Skill Distributions [article]

Ali Jadbabaie and Anuran Makur and Devavrat Shah
2020 arXiv   pre-print
The problem is, in essence, to learn a distribution from noisy, quantized observations.  ...  Our approach brings together prior work on learning skill parameters from pairwise comparisons with kernel density estimation from non-parametric statistics.  ...  a modern machine learning perspective.  ... 
arXiv:2006.08189v1 fatcat:5zeipcxr5rg7lfvok7cep55qva