Inproceedings,
Robust multi-task learning with \textlessi\textgreatert\textless/i\textgreater-processes
Proceedings of the 24th international conference on Machine learning, page 1103-1110. Corvalis, Oregon, ACM, (2007)
DOI: 10.1145/1273496.1273635
Abstract
Most current multi-task learning frameworks ignore the robustness issue, which means that the presence of öutlier" tasks may greatly reduce overall system performance. We introduce a robust framework for Bayesian multitask learning, t-processes (TP), which are a generalization of Gaussian processes (GP) for multi-task learning. TP allows the system to effectively distinguish good tasks from noisy or outlier tasks. Experiments show that TP not only improves overall system performance, but can also serve as an indicator for the "informativeness" of different tasks.
