|
| | | | |
| | | |
| Stat |
Members: 3667
Articles: 2'599'751
Articles rated: 2609
15 February 2025 |
|
| | | | |
|
Article overview
| |
|
|
Multi-Task Learning with Prior Information | | Mengyuan Zhang
; Kai Liu
; | | Date: |
4 Jan 2023 | | Abstract: | Multi-task learning aims to boost the generalization performance of multiple
related tasks simultaneously by leveraging information contained in those
tasks. In this paper, we propose a multi-task learning framework, where we
utilize prior knowledge about the relations between features. We also impose a
penalty on the coefficients changing for each specific feature to ensure
related tasks have similar coefficients on common features shared among them.
In addition, we capture a common set of features via group sparsity. The
objective is formulated as a non-smooth convex optimization problem, which can
be solved with various methods, including gradient descent method with fixed
stepsize, iterative shrinkage-thresholding algorithm (ISTA) with back-tracking,
and its variation -- fast iterative shrinkage-thresholding algorithm (FISTA).
In light of the sub-linear convergence rate of the methods aforementioned, we
propose an asymptotically linear convergent algorithm with theoretical
guarantee. Empirical experiments on both regression and classification tasks
with real-world datasets demonstrate that our proposed algorithms are capable
of improving the generalization performance of multiple related tasks. | | Source: | arXiv, 2301.01572 | | Services: | Forum | Review | PDF | Favorites | |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
|
| |
|
|
|
REGISTER