Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The authors seek to find training points that most contribute to a model’s failures. They do so by using continual learning techniques to identify the training examples with the highest influence for model failures. They then again use continual learning to try to “forget” features learned by these training examples. 
strengths_and_weaknesses: ## Strengths
*Quality:* I was relatively impressed by the experimental results of the method: particularly the results on identifying mislabeled examples and data poisoning. This certainly seems like a cheaper method for isolating influential points than retraining. 

*Originality/Significance:* The problem is relevant to the community, and the method seems to be sufficiently novel. Additionally, the framework for "forgetting" the features learned by particular datapoints is interesting. 

## Weaknesses
*Clarity*: Section 2.1 was not clearly written (see questions below). It may be helpful to start with why, even though $\mathcal{F}$ is in the test set, this method requires taking gradient steps on $\mathcal{F}$.  Providing additional intuition on EWC in the main paper would be helpful as well (this information is currently in the Appendix, but could be moved up in a compressed form).  

*Quality:* What happens when you apply this to an untouched CIFAR-10 dataset? Can you identify mislabeled examples within the dataset? Does your accuracy improve, or does performing continual learning in this setting reduce accuracy if there is no planted pathology?

questions: I had some questions on the proof/theoretical motivation in Section 2.1:
- The i.i.d modeling assumption you make seems to be (for two disjoint portions $D, \mathcal{C}$ of the training dataset) that $P(\mathcal{D}, \mathcal{C} \mid \theta) = P(\mathcal{D} \mid \theta) P(\mathcal{C} \mid \theta)$. Can you walk through why this assumption is justified, and when it might not be? ($\mathcal{C}$ and $\mathcal{D}$ are not from the same distribution, since $\mathcal{C}$ are the examples that cause failures). 
- In Example 1, you walk through how your method recovers linear influences. However, linear influences require taking a gradient step on the training example (so here z) and then measuring the change in prediction of the test example (so here $\mathcal{F}$). You seem to be doing the reverse. Are you recovering the transpose of the linear influence matrix?
- What is $z^*$ in line 140?

limitations: The authors did not address limitations of their work, or discuss negative societal impact. (If I missed it, please point it out in the author response)
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes a bayesian technique for determining the set of points that contribute to model errors and updating models to fix performance on the failure set while retaining performance. The key issue they overcome is determining the set of points that lead to errors is computationally hard, because you'd need to check every subset. Instead, they introduce a method that determines the set of points causing the errors in linear time, wrt dataset size. After they provide a technique to unlearn the datapoints in the failure set. Finally, they show their framework enables more efficient identification of the corrupt set in empirical evaluations over baselines such as random.


strengths_and_weaknesses: Strengths
- Interesting problem & quite relevant. The connections between influence functions are quite nice.
- Empirical evaluation is pretty comprehensive 

Weaknesses:
- The notation usage is a bit dense and hard to follow in places. Often found myself having difficulty recalling what terms mean. Also, some terms seem like they might be a bit overloaded. 
- The writing is hard to follow in places, and I found myself confused about what was going on several times while reading. This was particularly true in the empirical evaluations.

This paper is interesting overall and presents a seemingly useful method, but I'm having difficulty understanding the empirical evaluations in particular in several different places and found it quite hard to follow.

questions: - I'm a bit confused about the notation used with $p(F|D)$... This looks like the predictive distributon on the failure set given eq. 1, but its the predictive distribution of the model itself with the failure set. Could you clarify the usage here?
- I'm having a hard time following the Random Input Noise paragraph experiments. What is "salt and pepper"? It seems like ewc isn't detecting the corrupted images as efficiently as the baselines, what's going on here? 
- Also, the bar plots for accuracy in fig. 4 have large ranges making it look like the accuracies are quite close, but for ewc for mnist for example, there's a 3 point different from random, which is very large for mnist. Is this method having sigificant effects on performance?
- Why does the test set accuracy for fig 3 start at 100%? It seems surprising these models getting 100% test set accuracy on these datasets?

limitations: The authors have done a sufficient job here.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: The authors propose a Bayesian continual learning approach which aims to remove memories from a neural networks weights if those memories stem from data which ultimately hurts the performance of the model on some defined failure set.
strengths_and_weaknesses: # Strengths

- The method is well derived
- The experimental results show improvements over continual learning baselines.

# Weaknesses

- The datasets evaluated are quite small (MNIST, CIFAR-10). Given the method, it should be possible to use a pretrained model and apply it to a larger dataset such as ImageNet, correct? If that is the case, then couldn't a larger scale evaluation be done by taking the largest errors on the ImageNet validation set and applying the method and then measuring the difference between the Failure set and the remaining images in the ImageNet test set?

questions: - What are the concrete improvements over [2] which has a similar deletion approach?

- How does this approach compare to dataset condensation? It seems like a method of dataset condensation for a specific purpose.

- What is the underlying model which is used for CIFAR10? It looks like the 'original model' in figure 3 gets 100% accuracy, but the appendix quotes using ResNet50 from [1] where the a 50 layer network should give about 7% error rate.

- Were any of these results actually performed on a BNN with a distribution over the weights, or was the BNN interpretation only used for purposes of derivation?

- The legends in all charts in the main text are quite hard to read because of their small size, could the size of the legends and axes be increased?

- I find the notation and derivation of equations 5 and 6 to be too terse, and it took some effort to work out why they are true. Could a full derivation be included for the reader?

# References

[1] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

[2] Guo, C., Goldstein, T., Hannun, A., & Van Der Maaten, L. (2019). Certified data removal from machine learning models. arXiv preprint arXiv:1911.03030.

limitations: Yes, the authors have offered some limitations in Section 2.
review_title: null

Meta-review: metareview: This paper studies the setting where some data points are contaminated and as a result, the learned method suffers from performance degradation. Authors extend an existing continual learning algorithm called Elastic Weight Consolidation and use it for identifying and removing data points that are harmful to performance. The experiments confirms that their proposed methods can indeed identify the harmful data points and repair the learned model. All reviewers find this paper interesting and they are in agreement about accepting the paper. However, they are also in agreement that the paper is hard to follow. I suggest authors to take reviewers suggestions into account and improve the presentation for the camera-ready version to make it easier for the community to benefit from and build on this paper.
confidence: Certain
Decision: Accept