Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Transfer Learning via $\ell_1$ Regularization   Machine learning algorithms typically require abundant data under a
stationary environment. However, environments are nonstationary in many
real-world applications. Critical issues lie in how to effectively adapt models
under an ever-changing environment. We propose a method for transferring
knowledge from a source domain to a target domain via $\ell_1$ regularization.
We incorporate $\ell_1$ regularization of differences between source parameters
and target parameters, in addition to an ordinary $\ell_1$ regularization.
Hence, our method yields sparsity for both the estimates themselves and changes
of the estimates. The proposed method has a tight estimation error bound under
a stationary environment, and the estimate remains unchanged from the source
estimate under small residuals. Moreover, the estimate is consistent with the
underlying function, even when the source estimate is mistaken due to
nonstationarity. Empirical results demonstrate that the proposed method
effectively balances stability and plasticity.

11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys