Reviews For Paper
  Reviews For Paper
Paper ID 79
Title Curriculum Learning of Bayesian Network Structures

Masked Reviewer ID: Assigned_Reviewer_4
Review:
Question 
Relevance to ACML Good
Impact of Results Average
Application Novelty Average
Technical Novelty Good
Presentation Quality Good
Reproducibility Average
Overall Rating Accept
Confidence in Review Marginal
Detailed Comments This paper has proposed a heuristic algorithm exploiting the idea of curriculum learning to learn the Bayesian network structure. Theoretical analysis was given that the intermediate Bayesian structures are gradually approaching the target structure. Experiments were designed to compare the proposed algorithm and the state of the art -MMHC

This paper is well-written. Some minor suggestions are :

1. Page1 the last paragraph, PC and IC are the first time to use. Their full name should be given.

Please check the similar problems in the whole paper.

2. Eq.(7), the penalty term is a good solution, however, the author did not give an explanation why this specified form (Eq. 7) has been use rather than other?

Masked Reviewer ID: Assigned_Reviewer_5
Review:
Question 
Relevance to ACML Excellent
Impact of Results Good
Application Novelty Good
Technical Novelty Good
Presentation Quality Excellent
Reproducibility Good
Overall Rating Accept
Confidence in Review Average
Detailed Comments The main concern of this paper is the structure learning of Bayesian Networks. The authors introduced curriculum
learning strategy to this problem and proposed a learning algorithm in which greedy hill climbing technique was
used. The behavior of the learning algorithm was theoretically well investigated. Some numerical experiments shows a
convincing results.

The theorems in section 4 that guarantee a monotonicity of the estimation accuracy in the learning process are
interesting. Comparison to some of the different statistical approaches other than MMHC would strengthen the paper.

Masked Reviewer ID: Assigned_Reviewer_6
Review:
Question 
Relevance to ACML Good
Impact of Results Average
Application Novelty Average
Technical Novelty Average
Presentation Quality Good
Reproducibility Average
Overall Rating Neutral
Confidence in Review Average
Detailed Comments - This paper concentrates on constructing the topology of a BN given a set of random variables. It leverages curriculum learning with score-based approach to drive the search algorithm. The theory is provided to state that the Hamming and distribution distances of an intermediate BN, says G_i, to G_k is further than those of G_j to Gk provided i \leq j \leq k. This theoretical analysis somehow indicates that the intermediate states are gradually expanded and become more complicated. However, it cannot guarantee that the latter BNs gradually converge to the ground-truth BN.
- The paper is overall well-organized and well-presented. Nonetheless, some notations, e.g. the training set, domain for X_(i), and data segments should be explicitly declared and defined for comprehensibility.
The idea of conjoining the curriculum learning with score-based approach is nice and motivated. However, some heuristics that are difficult to verify are used in the paper, e.g. fixing X’(i) to different values, our learning target is actually the same DAG structure G_i but with different parameters (CPDs), use of all the training samples in learning Gi at each stage by revising the scoring function to take into account multiple versions of parameters. Apart from this, the role of curriculum learning is not much appealing. Actually, the proposed algorithm is more similar to part-by-part search than curriculum-based.


 
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