Lack of clarity:
The paper was difficult to follow, it omits several crucial details necessary to understand the proposed method.
Below are some general questions or suggestions:
While the paper's principal focus is on calibration and recalibration, it is unclear why there are claims to address aleatoric uncertainty
The concept of recalibration is introduced in Section 2 as a classification problem. However, in Section 4, the focus is on regression with normalizing flows
In Figures 1 and 2, what is X , c , W 1 , W 2 ?
Figure 2, add labels to x-axis and y-axis
Improve caption quality across all Figures and tables
CDF performance plot:
The paper claims the CDF performance plot is one of the main contributions, yet it is difficult to interpret or follow. Why are calibrated CDFs uniformly distributed?
What is σ ?
What is pull?
What is ψ
Recalibration Normalizing flows:
What are the complete formulations of the likelihoods optimized in 1) and 2)?
How is normalizing flow extended to multivariate calibration?
Weak experiments:
How is the Calib metric computed?
The experiments are on toy data and small UCI datasets. Additional large scale image datasets or regression tasks would strengthen the submission.
1) and2)? How is normalizing flow extended to multivariate calibration? Weak experiments: How is the Calib metric computed? The experiments are on toy data and small UCI datasets. Additional large scale image datasets or regression tasks would strengthen the submission.