Name
Abstract | ||
|---|---|---|
Most current multi-task learning frameworks ignore the robustness issue, which means that the presence of "outlier" 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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1145/1273496.1273635 | ICML |
Keywords | Field | DocType |
current multi-task,good task,multi-task learning,robust multi-task,robustness issue,bayesian multitask learning,robust framework,different task,outlier task,overall system performance,gaussian process,system performance,multi task learning | Multi-task learning,Pattern recognition,Computer science,Outlier,Robustness (computer science),Gaussian process,Artificial intelligence,Machine learning,Bayesian probability | Conference |
Citations | PageRank | References |
35 | 1.58 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shipeng Yu | 1 | 1767 | 118.84 |
| Volker Tresp | 2 | 2907 | 373.75 |
| Yu, Kai | 3 | 4799 | 255.21 |