Here are some interesting papers:
Kernel Selection:
Kernel Selection for Semi-Supervised Kernel Machines [Abstract][Paper]
Learning Nonparametric Kernel Matrices from Pairwise Constraints
| | [Abstract][Paper] | |
More Efficiency in Multiple Kernel Learning [Abstract][Paper]
Multiclass Multiple Kernel Learning [Abstract][Paper]
MTL and Transfer Learning:
Uncovering Shared Structures in Multiclass Classification [Abstract][Paper]
Discriminative Learning for Differing Training and Test Distributions [Abstract][Paper]
Boosting for Transfer Learning [Abstract][Paper]
Learning a Meta-Level Prior for Feature Relevance from Multiple Related Tasks [Abstract][Paper]
Multi-Task Learning for Sequential Data via iHMMs and the Nested Dirichlet Process [Abstract][Paper]
Self-taught Learning: Transfer Learning from Unlabeled Data [Abstract][Paper]
The Matrix Stick-Breaking Process for Flexible Multi-Task Learning [Abstract][Paper]
Asymptotic Bayesian Generalization Error When Training and Test Distributions Are Different [Abstract][Paper]
Robust Multi-Task Learning with $t$-Processes [Abstract][Paper]
Relational Learning and structured prediction
Relational Clustering by Symmetric Convex Coding [Abstract][Paper]
Fast and Effective Kernels for Relational Learning from Texts [Abstract][Paper]
Exponentiated Gradient Algorithms for Log-Linear Structured Prediction [Abstract][Paper]
Ranking:
Learning Random Walks to Rank Nodes in Graphs [Abstract][Paper]
Feature Selection:
Supervised Feature Selection via Dependence Estimation [Abstract][Paper]
Feature Selection in Kernel Space [Abstract][Paper]
Minimum Reference Set Based Feature Selection for Small Sample Classifications [Abstract][Paper]
Spectral Feature Selection for Supervised and Unsupervised Learning [Abstract][Paper]
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