Congratulations on your paper being accepted to NeurIPS, and thank you for sharing your code! I thought the task as described might be a good fit for a DEformer-like model (hereafter "DEformer-CSDI"), so I decided to run an experiment on the 10% missing healthcare dataset and I thought you might be interested in the results (code here). While my test set is identical to yours, I changed the training/validation split to 95%/5% and I used an online strategy to generate missing values for each training sample. Specifically, every time a training sample was encountered, I randomly selected 10% of the observed values to serve as the missing values.

Like the DEformer, the input for DEformer-CSDI consists of a mix of identity feature vectors and identity/value feature vectors. The difference in this case is that DEformer-CSDI is not learning the joint distribution, so only the identity feature vectors are included for the missing values and the attention mask is now full instead of lower triangular (i.e., every input can attend to every other input). Identity was encoded as f(t, k) = [t, embed(k)] where t and k are the time and feature indices, respectively, for a data point. One interesting difference between DEformer-CSDI and CSDI is that DEformer-CSDI simply ignores missing values that are not being predicted.

With no hyperparameter tuning, DEformer-CSDI achieves a mean absolute error of 0.219 on the 10% missing healthcare dataset. I thought it was notable that DEformer-CSDI outperformed the flattened Transformer baseline from Table 7 by a wide margin. With that being said, DEformer-CSDI is much larger than CSDI (19,250,493 parameters), so it would be interesting to see if CSDI's performance could be improved further using this online sampling strategy.

Thanks for your sharing and I tried the exe_physio.py in the "pretrained" mode， it had following error：

File "D:\Code\CSDI\CSDI-main\main_model.py", line 14, in init self.emb_feature_dim = config["model"]["featureemb"]

KeyError: 'featureemb'

Patching CVE-2007-4559

Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.