Paper: Tree-based Ensemble Learning for Out-of-distribution Detection (ICLR-2024)

Reviewer comment (Theory):
The idea that "out-of-distribution data may exhibit smaller Hamming distances among themselves" hinges on the assumption that the support of training and testing distributions does not overlap in each dimension. However, this assumption raises doubts as it prohibits anomalies from occurring in only one dimension.

Author response:
The reviewer mentioned Limitation 1,  but I’m not sure if I understand the issue. We assumed in Theorem 1 that the support of training and testing distributions does not overlap in each dimension, which means along all the dimensions, the two distributions do not overlap. We have rewritten the statement in Theorem 1 and hopefully it can address the confusion.

Reviewer comment (Evaluation):
It is advisable to present the experimental results in the format "mean ± std" due to the inherent randomness of random forests.

Author response:
We also added in the standard deviation for the experimental results in the appendix.

Reviewer comment (Novelty):
Decision tree learning and random forests should be traced back to the works of Quinlan (1979), Breiman et al. (1984), and Breiman (2001).

Author response:
We highly appreciate the reviewer for pointing out the misuse of the reference for decision tree and random forest, it is our carelessness for not making it correct in the first place, we have adapted these changes in the revised version of our paper.

Reviewer comment (Experiments):
There are no experiments on ImageNet benchmark.

Author response:
So far we haven’t perform the experiments on ImageNet benchmark, and we would like to add in more experiments in the final version of our paper.

Reviewer comment (Theory):
The results of the theoretical analysis are for a single classification tree model, not for a random forest.

Author response:
The reviewer also mentioned that the theoretical analysis are for a single classification tree model, not for a random forest. This is another great point. We have added a theorem (Theorem 4 in the updated version) to address this.

Reviewer comment (Writing):
For image classification, does the input $x$ refer to an image or a feature vector obtained from a pre-trained feature extractor?

Author response:
For input x in image classification, it refers to the latent feature vector obtained from an autoencoder.

Reviewer comment (Experiments):
Do the hyper-parameters used in training the tree models (such as tree depth, the number of terminal nodes, and the minimal size of terminal nodes) have any effect on OOD Detection results?

Author response:
From our experience when conducting the experiments, the hyperparameters used in training the tree models do not have much effect on the OOD detection results (as long as the parameters not too extreme). We have added these comments in the appendix of the updated version of our paper.