__ Summary and Contributions__: This paper proposed an extension of Lasso named as Transfer Lasso to improve prediction under changing environment, via incorporating two L1 regularization terms, one on the (present) model parameter, and another on the differences between "source" (previous) and "target" (present) model parameters.

__ Strengths__: 1. The problem is well motivated and the idea is clearly presented. Figures 1 and 2 help illustrating the ideas well.
2. The theoretical analyses are strong, with proofs on error bound, convergence, etc.
3. The paper is well written and easy to follow.

__ Weaknesses__: 1. While the presented model can be posed as a transfer learning problem. This paper is more about concept drift. Therefore, the title Transfer learning via l1 Regularization is a bit too broad and can be misleading for some readers.
2. In the experiments on concept drift and "transfer learning", only Lasso is the only method studied and compared. However, Lasso is not considered as a state-of-the-art (SOTA) for concept drift and transfer learning. Lasso is designed for neither of these problem. On the other hand, there are many other methods for concept drift and transfer learning, with some discussed in the Related Work section but none is compared against in the experiment.
3. There are many existing works on concept drift (e.g. twitter activities, anomaly detection), as the authors have cited. However, this paper studies only synthetic concept drift problems. It is not clear whether the proposed solution can deal with concept drift in real data successfully.
4. Similar to the above, there are many transfer learning benchmarks and methods. However, this paper studies only a synthetic example without comparing to any transfer learning methods (as said above, Lasso is not designed for transfer learning).
5. The end of Sec. 4.2 states that Transfer Lasso showed the best accuracy in feature screening. However, previous works on Lasso screening are not cited or compared, e.g. Ren et al. "Safe feature screening for generalized LASSO." TPAMI 40.12 (2017): 2992-3006.
6. Section 4.3 follows the experiments in [17]. However, the presented results did not include [17] (and related works on the same data) in comparison.
7. Line 253: how was the data divided into 30 batches?
8. Line 258: What is the cause of such computational instability for binary features? What are the ways to mitigate this problem?
9. Figure 5-right: annotations on the colours used are missing.
10. Minor issues. Typo. Line 179: unchaing

__ Correctness__: Some claims are misleading to me (see the above on weaknesses).

__ Clarity__: Yes, it was quite enjoyable to read, particularly the first three sections.

__ Relation to Prior Work__: Not really. Lasso is the primary prior work considered in this paper. Many related works are not discussed or compared.

__ Reproducibility__: Yes

__ Additional Feedback__:

__ Summary and Contributions__: In this paper, authors address the knowledge transfer via minimizing an empirical risk minimization with L1 regularization. Specifically, the method incorporates the L1 regularization of differences between source parameters and target parameters into the ordinary Lasso regularization, which ensures the sparsity of the parameters and limits the complexity of the model. This method can transfer knowledge from the past to the present in a stable environment. When the environment is unstable, it can discard the outdated knowledge and learn new knowledge, which realizes the adaptation of the model to the environment.

__ Strengths__: The authors proposed and analyzed the L1 regularization-based transfer learning framework. This applies to any parametric models, including GLM, GAM, and deep learning.

__ Weaknesses__: 1. The solution of the model is expressed unclearly in the initial coefficient estimation.
2. In the process of using the soft threshold function to solve the listed models, the value of the target coefficient is not given at the same time, and therefore, how are the values of the target coefficient other than the required \beta_j fixed? In addition, whether j here starts from 0 or arbitrarily specified, how to obtain the next j to be updated?
3. The sequence of cross-validation and soft threshold function for \beta is not clear.
4. The experiment is not convictive.

__ Correctness__: The idea of the model proposed in the article is reasonable. The solution is somewhat vague. The empirical methodology is correct.

__ Clarity__: The paper format and expression are rigorous, the theorem is proved in detail, and the algorithm part of the model solution is a bit vague.

__ Relation to Prior Work__: Before presenting the method in the paper, the article introduces the traditional method based on hypothesis testing to detect the concept drift problem and its limitations, and then introduces the currently proposed new tree-based and holistic-based methods to achieve empirical risk through L2 regularization Minimization, but these methods are not sparse, and small changes in parameters will cause changes in the model results. Later, the proposed transfer Lasso is presented.

__ Reproducibility__: Yes

__ Additional Feedback__: It is recommended that the author list the steps for solving the algorithm, which will make the order of the steps clearer.

__ Summary and Contributions__: This paper proves theoretical results for transfer learning in a high-dimensional linear regression setup with fixed design, using a Lasso-type penalization. More specifically, given an initial estimator \tilde\beta of the unknown parameter \beta^*, the updated estimator \hat\beta minimizes a Lasso-type loss function, that forces sparsity both of \hat\beta and of \tilde\beta-\hat\beta. An extra parameter \alpha (in addition to the usual Lasso tuning parameter \lambda) allows to balance those two penalties.
The error of the new estimate \hat\beta is bounded as a function of the error of the previous estimate \tilde\beta, under sub-Gaussianity of the noise and a generalized Restricted Eigenvalue condition for the fixed design. When there is no transfer (which corresponds to \alpha=1), the error seems to be similar to that of classical Lasso.

__ Strengths__: The paper is particularly well written, with a very clear exposition of the problem and of the theoretical results, as well as the empirical evaluations. The proofs are fairly simple, since they mostly follow the standard proofs of the Lasso estimator. Moreover (although I am not at all a specialist in this field), the use of a L1 penalty for transfer learning seems new in this paper.
In my opinion, this paper is absolutely relevant to the NeurIPS community.

__ Weaknesses__: I only have one major comment, about the GRE assumption: This assumption, in Theorem 1, actually depends on the error of the initial estimate (since the set \mathcal B depends on \Detla), and hence, it seems pretty artificial to me. Could the authors elaborate a bit? For instance, if the entries of the matrix X are iid Gaussian and independent of the initial estimate, does the Assumption hold with high probability?
A few minor comments:
1. In theorem 2, could you be a little more precise with the asymptotics (what quantities grow to infinity among n, p, s, and how?)
2. Line 164, replace \alpha with n in the subscript of \nu.
3. Line 179, there is a typo in the word "unchanging"
4. Right after Theorem 4, could you elaborate a little more about why it is useful?

__ Correctness__: The claims seem all correct to me.

__ Clarity__: The paper is very well written. Maybe it could be useful if a little more details (or arguments) were given in the proofs (such as mentioning Holder's inequality when it is used, etc.)

__ Relation to Prior Work__: It does seem clearly discussed, although, again, I am not 100% familiar with this literature (neither Lasso nor transfer learning).

__ Reproducibility__: Yes

__ Additional Feedback__:

__ Summary and Contributions__: ---- After acknowledgement of the authors' rebuttal ----
After discussing with the fellow reviewers and considering the authors' rebuttal, I am now convinced that this submission is a worthwhile contribution.
My remaining concerns are the following.
1. Motivation: the paper would be much better off if the authors gave stronger motivations for their model. Otherwise, it feels like, "Transfer Lasso is what we know how to do and analyze, therefore it will certainly have some applications somewhere"...
2. Applicability of the theoretical results: I am no longer so familiar with these types of assumptions. The authors could make an attempt to clarify when these assumptions are likely to hold, and when not.
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The papers proposes a transfer learning approach between a source and a target domain of equal dimensions. The method is based on the Lasso estimator augmented by an $ell_1$ penalty term that encourages the target parameters to be close to the source ones. Theoretical and empirical results are provided.

__ Strengths__: The approach is straightforward and leverages previous theoretical results for the lasso. The optimization procedure is also simple and scalable.

__ Weaknesses__: The approach is seemingly too simple and not well-motivated: Why should the source and target parameters be close in $ell_1$ norm?
The experimental results are too limited:
- there is no assessment of how the sample size affects performance
- no state-of-the art algorithms are being compared to, despite being mentioned throughout the paper
No comparisons with transfer learning approaches that learn a common dictionary among the tasks, e.g., Maurer et al, Sparse coding for multitask and transfer learning, ICML 2013 and others.

__ Correctness__: The optimization procedure is correct. The theoretical findings appear reasonable, however all the proofs are in the appendix, and honestly I did not check them line by line.
The empirical methodology is insufficient: no impact of sample size is evaluated, and no other state-of-the-art transfer learning approaches have been considered.

__ Clarity__: The paper is clear and well written. Personally, I would have preferred more discussions of the results, both theoretical and empirical.

__ Relation to Prior Work__: While some previous literature has been properly discussed, many approaches to transfer learning have been ignored, most notably the ones relying on learning shared dictionaries among tasks. Furthermore, in the experimental section, the authors compare only against the Lasso, either using only the current task's training set or all the training sets combined.

__ Reproducibility__: No

__ Additional Feedback__: I would suggest the authors to first find stronger motivations for their approach and then perform a more exhaustive empirical comparison, especially on real datasets.