Kati8/ICLR_2023_2025 huggingface dataset:
authors: Yichen Wu
authors: Long-Kai Huang
authors: Renzhen Wang
authors: Deyu Meng
authors: Ying Wei
reviews: 
Comments: This paper focused on the branch of Meta-Continual Learning (Meta-CL) methods in the context of Continual Learning (CL). By characterizing the Meta-CL algorithms as a new perspective of up-to-date Hessian matrix approximation, the authors tried to bridge the gap between the Meta-CL and the regularized-based CL methods. Under this viewpoint, Meta-CL implicitly approximated the Hessian in an online manner through the use of hypergradient in the bi-level optimization process. To address the erroneous information during the Hessian estimation due to the sampling process from the random memory buffer, the authors proposed Variance Reduced Meta-CL (VR-MCL) to control the high variance of the hypergradient under online continual learning. With a theoretical analysis, the authors showed that the proposed VR-MCL is equivalent to the inclusion of a penalty term within the implicit Hessian estimation in Meta-CL. The experimental results on three benchmarks indicated that the proposed method outperformed the regularization-based and Meta-CL baselines.
Strengths: 1. The motivation of this work is clear and easy to follow.
2. It is interesting to see that an inherent connection can be built between the regularization-based methods and Meta-CL methods via the roles of the Hessian information in these two methodological streams.
3. This work provided theoretical analyses and empirical verifications to help to better understand the motivation.
Weaknesses: 1. Most parts of the mathematical derivations are easy to follow. However, some detailed notations are not clear in the context, which reduces the readability.
2. The motivation of some experimental designs was not too clear, such as the imbalance CL setting.
3. It seems the math derivation process needs some strict assumptions. I doubt the gap between the theoretical findings and the empirical applications. 

See the Questions part for more details.
Recommendation: 8: accept, good paper
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 3 good
Clarity: 3 good
reviews: 
Comments: The paper introduces a novel approach called VRMCL (Variance Reduced Meta Continual Learning), integrating a hyper-gradient variance reduction technique for Meta Continual Learning (CL). Furthermore, it offers theoretical regret bounds for the proposed method. The paper extensively evaluates the VRMCL method across three datasets, with diverse continual learning scenarios.
Strengths: 1. Clarity: The paper is well written and easy to follow.
2. Technical Proficiency: The paper showcases a highly technical.
3. Originality and Novelty: The paper introduces a novel concept focused on diminishing variance in gradient computations concerning memory buffers in online settings.
4. Comprehensive Empirical Validation: The paper includes extensive experiments and comprehensive ablation study which support the claims made in the paper.
Weaknesses: 1. Limited Comparison:
    1. While the authors have made comparisons with recent baselines, the paper could benefit from a more extensive comparison by including well-established methods such as FTML[1] and LFW[2]. A broader comparison would provide a more comprehensive evaluation of the proposed method's strengths and weaknesses.
2. Limited Experimental Width:
    1. Although the authors have conducted evaluations on popular datasets like CIFAR10, CIFAR100, and TinyImageNet, it would be good to test the effectiveness of the proposed method on larger datasets, such as ImageNet-1K. This would offer insights into the algorithm's performance in handling catastrophic forgetting in longer sequences.
    2. Additionally, the experiments could be enhanced by varying the number of tasks on each dataset, thereby showcasing the adaptability of VR-MCL under different task configurations.
3. Lack of Memory Update Strategy Explanation:
    1. The paper could benefit from a more thorough explanation of the memory update strategy employed in the VR-MCL algorithm. Given the algorithm's reliance on the Memory Buffer, a clearer and more detailed description of the update mechanism is essential to provide a comprehensive understanding of the methodology.

[1] Finn, C., Rajeswaran, A., Kakade, S., & Levine, S. (2019, May). Online meta-learning. In International Conference on Machine Learning (pp. 1920-1930). PMLR.

[2] Li, Z., & Hoiem, D. (2017). Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence, 40(12), 2935-2947.
Recommendation: 8: accept, good paper
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 3 good
Clarity: 3 good