Comments (5)
Adversarial loss effects less in missing completely at random.
When the missingness comes from missing at random or missing not at random settings, the effects of adversarial loss increase.
You can try different datasets with different missingness settings.
from gain.
Well noticed. Thank you very much for your reply!
from gain.
Dear all, please bear me for adding question after this issue was closed, but I think my question is relevant so I post it here.
I was wondering that Proposition 2 requires that M and X are independent? How could the theoretical analysis be adapted to the missing not at random mechanism (MNAR)?
In table 3 of supplementary document, indeed we can see that GAIN is much better than auto-encoder under MNAR. Does the implementation of auto-encoder use M as additional input? Or it is a simple implementation with only X as input?
from gain.
- We only prove the theoretical works in missing completely at random setting.
- Thus, it is not directly adapted to MNAR and MAR settings.
- We use M as the additional inputs for MNAR and MAR settings that we would like to capture the information in the mask vector.
from gain.
Thanks for your prompt reply. From the results, it is a remarkable feature of GAIN for handling MNAR or MAR!
from gain.
Related Issues (20)
- How to decide Missingness Mechanism HOT 1
- Differences with the paper HOT 1
- Using GAIN in inductive mode HOT 1
- Changing only missing values? and scoring? HOT 1
- Why not both L_G and L_D relevant to V(D,G)? HOT 1
- Could you please provide Requirements.txt file HOT 1
- My dataset is 203454KB, I can't get the dataset after filling, because my dataset is too big? It gives some mistakes. HOT 1
- mixed (categorical and numerical) data HOT 3
- Model for the MNIST dataset HOT 1
- alpha HOT 1
- original data HOT 1
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- hyperparameters HOT 3
- RMSE is not stable HOT 1
- RMSE HOT 1
- Hint matrix HOT 1
- Why isn't the loss calculated only with b_i=0 values of the Hints. HOT 2
- No split training and testing sets? HOT 2
- Training Query HOT 1
- about minibatch HOT 1
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from gain.