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- W2020110803 abstract "Most current multi-task learning frameworks ignore the robustness issue, which means that the presence of 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." @default.
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- W2020110803 date "2007-01-01" @default.
- W2020110803 modified "2023-10-17" @default.
- W2020110803 title "Robust multi-task learning with<i>t</i>-processes" @default.
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- W2020110803 doi "https://doi.org/10.1145/1273496.1273635" @default.
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