Multi-Task Learning and Algorithmic Stability
Authors: Yu Zhang
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study multi-task algorithms from the perspective of the algorithmic stability. We give a deļ¬nition of the multi-task uniform stability, a generalization of the conventional uniform stability, which measures the maximum difference between the loss of a multi-task algorithm trained on a data set and that of the multitask algorithm trained on the same data set but with a data point removed in each task. In order to analyze multi-task algorithms based on multi-task uniform stability, we prove a generalized Mc Diarmid s inequality which assumes the difference bound condition holds by changing multiple input arguments instead of only one in the conventional Mc Diarmid s inequality. By using the generalized Mc Diarmid s inequality as a tool, we can analyze the generalization performance of general multitask algorithms in terms of the multi-task uniform stability. Moreover, as applications, we prove generalization bounds of several representative regularized multi-task algorithms. |
| Researcher Affiliation | Academia | Yu Zhang Department of Computer Science, Hong Kong Baptist University The Institute of Research and Continuing Education, Hong Kong Baptist University (Shenzhen) |
| Pseudocode | No | The paper contains mathematical definitions, theorems, and proofs, but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention releasing any open-source code. |
| Open Datasets | No | The paper is theoretical and does not describe or use specific datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe the hardware used for any experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |