Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 6: Weak Accept
review_content: the paper proposes an algorithm that adversarially filters out examples to reduce dataset-specific spurious bias. the key intuition is that the datasets are curated in a way that easy to obtain samples have higher probability to be admitted to the dataset. however, not all real world samples are easy to obtain. in other words, real world samples may follow a completely different distribution than curated samples with easy-to-obtain ones.

the proposed approach discounts the data-rich head of the datasets and emphasizes the data-low tail. they quantify data-rich / data-low by the best possible out-of-sample classification accuracy achievable by models when predicting. 

then adjust the dataset via the expected out-of-sample classification accuracy. the idea of the paper is interesting and the experiments show a substantial reduction in the performance of existing algorithms. this make the paper a promising proposal.

review_title: Official Blind Review #2

final_score: rating: 6: Weak Accept
review_content: Summary: This paper hypothesizes that even though we are able to achieve very impressive performance on benchmark datasets as of now (e.g. image net), it might be due to the fact that benchmarks themselves have biases. They introduce an algorithm that selects more representative data points from the dataset that allow to get a better estimate of the performance in the wild. The algorithm ends up selecting more difficult/confusing instances.

This paper is easy to read and follow (apart from some hickup with a copy of three paragraphs), but in my opinion of limited use/impact.

Comments:
1) There is a repetition of the " while this expression formalizes.." paragraph and the next paragraph and the paragraph "As opposed to .." is out of place. Please fix
2) I am not sure 
- What applications the authors suggest. They seem to say that benchmark authors should run their algorithm and make benchmarks harder. To me it seems that benchmarks become harder because you remove most important instances from the training data (so Table 4 is not surprising - you remove the most representative instances so the model can't learn)
- how practically feasible it is.  Even if in previous point I am wrong, the algo requires retraining the models on subsets (m iterations). How large is this m?
3) Other potential considerations:
-  When you change the training size, the model potentially needs to be re-tuned (regularization etc) (although it might be not that severe since the size of the training data is preserved at t)
- How do u chose the values of hyperparams (t, m,k eta), how is performance of your algorithm depends on it
4) I don't see any good baselines to compare with - what if i just chose instances that get the highest prediction score on a model and remove these. How would that do? For NLP (SNLI) task i think this would be a more reasonable baseline than just randomly dropping the instances,
5) I wonder if you actually retrain the features after creating filtered dataset, new representation would be able to recover the performance. 

I read authors rebuttal and new experiments that show that the models trained on filtered data generalize better are proving the point, thanks. Changing to weak accept

review_title: Official Blind Review #1

final_score: rating: 6: Weak Accept
review_content: This paper proposes to learn a subset of a given dataset that acts as an adversary, that hurts the model performance when used as a training dataset. The central claim of the paper is that existing datasets on which models are trained are potentially biased, and are not reflective of real world scenarios. By discarding samples that add to this bias, the idea is to make the model perform better in the wild. The authors propose a method to do so, and then refine it so that the resulting solution is tractable. They implement the method on several datasets and show that by finding these adversarial samples, they indeed hurt model performance. 

COMMENTS:
- Overall the method seems to be something like what is done in k-fold CV, except here we want to find a subset that is the worst at predicting model performance. To this end, I find the introduction of terms like "representation bias" and "predictability scores" unnecessary. Why not model the entire problem in terms of classification error? 

- Page 3 : the last 2 paragraphs are repeated from above. 

E: i read the author responses and they addressed my concern about model performance in the wild. I have updated my score to reflect this. 

- eqn (3) and the set of equations above: for the math to work, you need q(i) to have non-zero support on all samples. To that end, the sentence that says it works for "any" q() is incorrect. 

- The experiments back your claim that your method makes the data more challenging to train on. But that does not address the central idea, that the resultant models do better in the wild. If the aim is to make the models robust to real world, you have provided no evidence that your method does so. 

- Table 1: the D_92k column is good comparison to have. Thanks. 


review_title: Official Blind Review #3

Meta-review: This paper proposes to address the issue of biases and artifacts in benchmark datasets through the use of adversarial filtering. That is, removing training and test examples that a baseline model or ensemble gets wright. 

The paper is borderline, and could have flipped to an accept if the target acceptance rate for the conference were a bit higher. All three reviewers ultimately voted weakly in favor of it, especially after the addition of the new out-of-domain generalization results. However, reviewers found it confusing in places, and R2 wasn't fully convinced that this should be applied in the settings the authors suggest. This paper raises some interesting and controversial points, but after some private discussion, there wasn't a clear consensus that publishing it as is would do more good than harm.
Decision: Reject