Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Approximating Probabilistic Inference in Bayesian Belief Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
The smoothed dirichlet distribution: understanding cross-entropy ranking in information retrieval
The smoothed dirichlet distribution: understanding cross-entropy ranking in information retrieval
Learning to rank: from pairwise approach to listwise approach
Proceedings of the 24th international conference on Machine learning
Listwise approach to learning to rank: theory and algorithm
Proceedings of the 25th international conference on Machine learning
Free energy estimates of all-atom protein structures using generalized belief propagation
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Minimizing and learning energy functions for side-chain prediction
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
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Given multiple possible models b1, b2, ... bn for a protein structure, a common sub-task in in-silico Protein Structure Prediction is ranking these models according to their quality. Extant approaches use MLE estimates of parameters ri to obtain point estimates of the Model Quality. We describe a Bayesian alternative to assessing the quality of these models that builds an MRF over the parameters of each model and performs approximate inference to integrate over them. Hyperparameters w are learnt by optimizing a list-wise loss function over training data. Our results indicate that our Bayesian approach can significantly outperform MLE estimates and that optimizing the hyper-parameters can further improve results.