Weakness: 1. The authors should provide justification of choosing to use AAE in the work. In particular, why is AAE an attractive approach for MTPP? 2. The authors mention: "The typical MTTP baselines like Reinforcement Learning, RNNs, Wasserstein GANs require a significant amount of data to train a network. Therefore, such techniques are not applicable to our proposed approach. As the baseline, we compare the proposed data generation technique with a Markov chain approach which was applied to the same dataset in (Klausen et al., 2018b)." The authors could try to apply Reinforcement Learning, RNNs, Wasserstein GANs with data filled by simple methods, to empirically validate that these methods are not suitable for incomplete, small data. 3. In feature mapping method, the authors could provide justification for converting marked point times t_ij to days a_i. Besides, the method to convert t_ij to a_i and examples are not provided. If this is a common preprocessing method in MTPP, the authors should cite the relevant work. Overall, Step 1 of Algorithm 1 is not clear. 4. At the data approximation technique (step 3 of the Algorithm 1), the author randomly chooses a probability for the appearance of an unobservable data point but there is a lack of explanations. Can the authors explain the reason of selecting from [0, Pcj(0)]? 5. The authors should provide more details of incomplete and small dataset, and compare with their method when training with full data. This is to understand that if generated data is still good when training with small dataset
=============== after rebuttal: I thank authors for the responses. After reviewing the authors' response and other reviewers' comments, I keep my original rating.
1. The authors should provide justification of choosing to use AAE in the work. In particular, why is AAE an attractive approach for MTPP?