Paper:
Explaining Deep Learning Models -A Bayesian Non-parametric Approach
Understanding and interpreting how machine learning (ML) models make decisions have been a big challenge. While recent research has proposed various technical approaches to provide some clues as to how an ML model makes individual predictions, they cannot provide users with an ability to inspect a model as a complete entity. In this work, we propose a novel technical approach that augments a Bayesian non-parametric regression mixture model with multiple elastic nets. Using the enhanced mixture model, we can extract generalizable insights for a target model through a global approximation. To demonstrate the utility of our approach, we evaluate it on different ML models in the context of image recognition. The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.

Review:
I think the rebuttal is prepared very well. Although the assumption of a single component approximating the local decision boundary is quite strong, the paper nonetheless offers a good, systematic approach to interpreting black box ML systems. It is an important topic and I don't see a lot of studies in this area.  Overview  In an effort to improve scrutability (ability to extract generalizable insight) and explainability of a black box target learning algorithm the current paper proposes to use infinite Dirichlet mixture models with multiple elastic nets (DMM-MEN) to map the inputs to the predicted outputs. Any target model can be approximated by a non-parametric Bayesian regression mixture model. However, when samples exhibit diverse feature correlations, sparsity, and heterogeneity standard non-parametric Bayesian regression models may not be sufficients. The authors integrate multiple elastic nets to deal with diverse data characteristics. Extensive experiments are performed on two benchmark data sets to evaluate the proposed method in terms of scrutability and explainability. For scrutability a bootstrap sample of the training set is used to replace the most important pixels (as determined by the proposed algorithm) in images with random values. The intuition behind this exercise is that if the selected pixels are indeed strong features then the classification accuracy should suffer significantly and the degree of impact should outweigh other scenarios with randomly manipulated features. This intuition is demonstrated to be correct on both data sets. For explainability the proposed method is compared against two other methods from the literature (LIME and SHAP). The proposed method can identify parts of images that contribute to decisions with greater level accuracy than the other two techniques. Results suggest that the proposed technique can also be used to identify anomalies (inconsistent predictions of the target system) with some level of success.     Technical Quality  I would consider the technical quality to be good with some reservations.    The main idea relies on the assumption that one component in the mixture model can be sufficient to explain a single prediction. What happens if a single component cannot approximate the local decision boundary near a single instance with an acceptable accuracy? In reality one would expect that many Normal components might be needed to explain a single prediction. So using a single component can make the prediction uninterpretable. What aspect of the model guarantee or stipulate sparse mixture components?   The motivation behind using Orthant Gaussian prior on regression coefficients is not well justified. Does this really serve for its purpose (data dimensionality and heteregoneity)? What would happen if standard Gaussian prior was used as a prior?  I also do not follow the need for truncation when the finite number of components can be easily determined during inference without any truncation especially when MCMC inference is used.      Clarity  The paper reads well with some minor issues outlined below (list is not exhaustive).  Line 48 Most of the works that related to, "that" here is redundant Line 81 any learning models, "model"  Line 320 when it comes specific datasets, "to" missing Line 320 it dose not affect, "does"    Originality  I would consider both the method (Dirichlet mixture models with multiple elastic nets) and the approach (evaluation in terms of scrutinability and explainability) quite original.  Significance:  I expect the significance of this work to be high as there is a dire need in the ML literature for models that can make outputs of complex learning systems more interpretable. Toward achieving this end the current paper proposes a systematic approach to explain and scrutinize the outputs of deep learning classifiers with promising results.     Other Comments:  Please define PCR (Principal Component Regression) and explain why PCR is used as opposed to classification accuracy