When performing multi-task predictions with time-series data, knowledge learned for one task at a specific time step may be useful in learning for another task at a later time step (e.g. prediction of sepsis may be useful for prediction of mortality for risk prediction at intensive care units). To capture such dynamically changing asymmetric relationships between tasks and long-range temporal dependencies in time-series data, we propose a novel temporal asymmetric multi-task learning model, which learns to combine features from other tasks at diverse timesteps for the prediction of each task. One crucial challenge here is deciding on the direction and the amount of knowledge transfer, since loss-based knowledge transfer (Lee et al., 2016; does not apply in our case where we do not have loss at each timestep. We propose to tackle this challenge by proposing a novel uncertaintybased probabilistic knowledge transfer mechanism, such that we perform knowledge transfer from more certain tasks with lower variance to uncertain ones with higher variance. We validate our Temporal Probabilistic Asymmetric Multi-task Learning (TP-AMTL) model on two clinical risk prediction tasks against recent deep learning models for time-series analysis, which our model significantly outperforms by successfully preventing negative transfer. Further qualitative analysis of our model by clinicians suggests that the learned knowledge transfer graphs are helpful in analyzing the model's predictions.
The submission proposes a probabilistic method for multitask learning for sequence data focusing on the use of uncertainty to control the amount of transfer between tasks. In comparison to prior work, the approach directly models uncertainty by parametrising Gaussian distributions and dropout variational inference. The approach additionally models transfer between features from different time-steps. The evaluation shows that the method outperforms recent state-of-the-art models on two medical benchmarks.
The paper is overall well written but includes some vagueness regarding the proposed method as well as the experimental setup. While describing techniques to model two kinds of uncertainty the aspect of the model which adapts transfer to the amount of uncertainty in source and target features is not described in detail (F_theta). It is described as if it only received samples for both features without any additional information regarding uncertainty. This makes it improbable that the model is able to take the feature’s uncertainty into account when modelling transfer.
The paper follows in section 3.3 with an argument for the lower computational complexity of only transferring from previous time-steps. I might misunderstand the description but having every time-step’s features be influenced only by previous timesteps should still result in complexity O(T^2) (even though the amount of computation is reduced by a constant factor). 
My background is not in machine learning with clinical data (so I do not know common datasets) but it is unclear to me why the datasets evaluated on only consist of a very small section of the overall dataset they’re taken from (in case 1). Looking at table 4 the main factor seems to be probabilistic modelling so additional ablations with either version of uncertainty would be interesting.
By introducing probabilistic modelling to control the amount of transfer between tasks the paper provides an interesting perspective and is able to show strong performance. However it is also quite vague on important aspects such as the exact modelling of transfer and limited regarding evaluation.
Minor aspects:
- V from the final equation on page 5 is never described.
- After describing modelling both kinds of uncertainty in the method section, the reader is uninformed which uncertainty is shown in plots ⅘
- Table 4 does not have the two highest results in bold (see AMTL samestep)
- Very limited multitask and transfer learning references pre 2016.
- Some descriptions are unclear including the mention of ‘attention allocation’