From: AAAI.org/2022/Workshop/AdvML/Program_Chairs
title: Paper Decision
decision: Accept (Long Paper)
comment: Both reviewers agree to accept this paper. The weaknesses can be addressed in the camera-ready version.

From: AAAI.org/2022/Workshop/AdvML/Paper20/Reviewer_6qUn
title: An ensemble training method for robustness
review: This paper proposes an ensemble training method to enhance the robustness. The ensemble training is implemented by training a super-net called random gated network (RGN). An interesting block called random gated blocks (RGB) is proposed to diversify the vulnerability of different paths through the RGN. Extensive experiments demonstrate the effectiveness of RGN. 

Weakness:
There are too many typos in this paper. Please correct them. For example, “Pan et. al. treat the distribution” in Page 3 (should be Pang et. al.); Algorithm 1 (incorrect line numbers, redundant right bracket, meaningless “=0” in last line)

rating: 6: Marginally above acceptance threshold
confidence: 5: The reviewer is absolutely certain that the evaluation is correct and very familiar with the relevant literature

From: AAAI.org/2022/Workshop/AdvML/Paper20/Reviewer_Fxua
title: An simple yet efficient ensemble training method.
review: Pros:
1, The proposed ensemble training method is simple yet efficient. Compared with conventional methods, this method is easier to scale up and faster.
2, The paper is easy to follow.
3, Most of the experiments are convincing.

Cons:
1, Though one advantage of the proposed method is easy-to-scale-up. However, it is also admitted that the scaling-up in the method brings no benefit, which is disappointing.
2, Since one contribution is minimal computational overhead, the paper should offer the comparison on training speed with other methods.
3, The proposed method cannot support the ensemble of models in different architectures.
4, The algorithm 1 is not formal.

rating: 8: Top 50% of accepted papers, clear accept
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct