Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-Task Learning and Algorithmic Stability
Authors: Yu Zhang
AAAI 2015 | Venue PDF | 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 de๏ฌnition 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. |