Kernel Change-point Detection with Auxiliary Deep Generative Models
Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on the distributions than traditional parametric approaches. However, selecting kernels is non-trivial in practice. Although kernel selection for the two-sample test has been studied, the insufficient samples in change point detection problem hinder the success of those developed kernel selection algorithms. In this paper, we propose KL-CPD, a novel kernel learning framework for time series CPD that optimizes a lower bound of test power via an auxiliary generative model. With deep kernel parameterization, KL-CPD endows kernel two-sample test with the data-driven kernel to detect different types of change-points in real-world applications. The proposed approach significantly outperformed other state-of-the-art methods in our comparative evaluation of benchmark datasets and simulation studies.
 + Using a generative model as the surrogate distribution for kernel two-sample test is novel + An important and new application of deep generative models + Strong experiments on synthetic and real-world time series data sets + Very clear writing and explanation of the idea - reply sample segments from both directions (past and future) while in the practical setting, CPD is usually sequential and in one directional - lack theoretical understanding of the limit of the neural-generator in the kernel two-sample test
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Kernel Change-point Detection with Auxiliary Deep Generative Models
Detecting the emergence of abrupt property changes in time series is a challenging problem. Kernel two-sample test has been studied for this task which makes fewer assumptions on the distributions than traditional parametric approaches. However, selecting kernels is non-trivial in practice. Although kernel selection for the two-sample test has been studied, the insufficient samples in change point detection problem hinder the success of those developed kernel selection algorithms. In this paper, we propose KL-CPD, a novel kernel learning framework for time series CPD that optimizes a lower bound of test power via an auxiliary generative model. With deep kernel parameterization, KL-CPD endows kernel two-sample test with the data-driven kernel to detect different types of change-points in real-world applications. The proposed approach significantly outperformed other state-of-the-art methods in our comparative evaluation of benchmark datasets and simulation studies.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 + Using a generative model as the surrogate distribution for kernel two-sample test is novel + An important and new application of deep generative models + Strong experiments on synthetic and real-world time series data sets + Very clear writing and explanation of the idea - reply sample segments from both directions (past and future) while in the practical setting, CPD is usually sequential and in one directional - lack theoretical understanding of the limit of the neural-generator in the kernel two-sample test
assistant
 + Using a generative model as the surrogate distribution for kernel two-sample test is novel + An important and new application of deep generative models + Strong experiments on synthetic and real-world time series data sets + Very clear writing and explanation of the idea - reply sample segments from both directions (past and future) while in the practical setting, CPD is usually sequential and in one directional - lack theoretical understanding of the limit of the neural-generator in the kernel two-sample test