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Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests
Sean Kulinski · Saurabh Bagchi · David Inouye

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1600

While previous distribution shift detection approaches can identify if a shift has occurred, these approaches cannot localize which specific features have caused a distribution shift---a critical step in diagnosing or fixing any underlying issue. For example, in military sensor networks, users will want to detect when one or more of the sensors has been compromised, and critically, they will want to know which specific sensors might be compromised. Thus, we first define a formalization of this problem as multiple conditional distribution hypothesis tests and propose both non-parametric and parametric statistical tests. For both efficiency and flexibility, we then propose to use a test statistic based on the density model score function (i.e. gradient with respect to the input)---which can easily compute test statistics for all dimensions in a single forward and backward pass. Any density model could be used for computing the necessary statistics including deep density models such as normalizing flows or autoregressive models. We additionally develop methods for identifying when and where a shift occurs in multivariate time-series data and show results for multiple scenarios using realistic attack models on both simulated and real-world data.

Author Information

Sean Kulinski (Purdue University)
Saurabh Bagchi (Purdue University)

Saurabh Bagchi is a Professor in the School of Electrical and Computer Engineering and the Department of Computer Science at Purdue University in West Lafayette, Indiana. He is the founding Director of a university-wide resilience center at Purdue called CRISP (2017-present). He is the recipient of the Alexander von Humboldt Research Award (2018), the Adobe Faculty Award (2017), the AT&T Labs VURI Award (2016), the Google Faculty Award (2015), and the IBM Faculty Award (2014). He was elected to the IEEE Computer Society Board of Governors for the 2017-19 term and re-elected in 2019. He is an ACM Distinguished Scientist (2013), a Senior Member of IEEE (2007) and of ACM (2009), and a Distinguished Speaker for ACM (2012). He is a co-lead on the $39M WHIN-SMART center at Purdue. Saurabh's research interest is in dependable computing and distributed systems. He is proudest of the 21 PhD students and 50 Masters thesis students who have graduated from his research group and who are in various stages of building wonderful careers in industry or academia. In his group, he and his students have way too much fun building and breaking real systems. Along the way this has led to 10 best paper awards or nominations at IEEE/ACM conferences. Saurabh received his MS and PhD degrees from the University of Illinois at Urbana-Champaign and his BS degree from the Indian Institute of Technology Kharagpur, all in Computer Science. He was selected as the inaugural International Visiting Professor at IIT Kharagpur in 2018.

David Inouye (Purdue University)

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