CODiT: Conformal Out-of-Distribution Detection in Time-Series Data for Cyber-Physical Systems

Kaur, Ramneet; Sridhar, Kaustubh; Park, Sangdon; Yang, Yahan; Jha, Susmit; Roy, Anirban; Sokolsky, Oleg; Lee, Insup

doi:10.1145/3576841.3585931

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Only those using RAPS matched the expected performance of having no prediction display in hard OOD instances with larger set sizes. Our results underscore the potential value of treating the detection of instance type as a model's learning problem (e.g., [32]).…”

Section: Discussionsupporting

confidence: 54%

Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling

Zhang,

Chatzimparmpas,

Kamali

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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As deep neural networks are more commonly deployed in highstakes domains, their black-box nature makes uncertainty quantification challenging. We investigate the effects of presenting conformal prediction sets-a distribution-free class of methods for generating prediction sets with specified coverage-to express uncertainty in AI-advised decision-making. Through a large online experiment, we compare the utility of conformal prediction sets to displays of Top-1 and Top-𝑘 predictions for AI-advised image labeling. In a pre-registered analysis, we find that the utility of prediction sets for accuracy varies with the difficulty of the task: while they result in accuracy on par with or less than Top-1 and Top-𝑘 displays for easy images, prediction sets excel at assisting humans in labeling out-of-distribution (OOD) images, especially when the set size is small. Our results empirically pinpoint practical challenges of conformal prediction sets and provide implications on how to incorporate them for real-world decision-making. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Empirical studies in HCI; Visualization design and evaluation methods; Empirical studies in visualization.

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Section: Discussionsupporting

confidence: 54%

Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling

Zhang,

Chatzimparmpas,

Kamali

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

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Lightning Talk: Trinity - Assured Neuro-symbolic Model Inspired by Hierarchical Predictive Coding

Jha

2023

2023 60th ACM/IEEE Design Automation Conference (DAC)

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Memory-based Distribution Shift Detection for Learning Enabled Cyber-Physical Systems with Statistical Guarantees

Yang,

Kaur,

Dutta

et al. 2024

ACM Trans. Cyber-Phys. Syst.

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Incorporating learning based components in the current state-of-the-art cyber physical systems (CPS) has been a challenge due to the brittleness of the underlying deep neural networks. On the bright side, if executed correctly with safety guarantees, this has the ability to revolutionize domains like autonomous systems, medicine and other safety critical domains. This is because, it would allow system designers to use high dimensional outputs from sensors like camera and LiDAR. The trepidation in deploying systems with vision and LiDAR components comes from incidents of catastrophic failures in the real world. Recent reports of self-driving cars running into difficult to handle scenarios is ingrained in the software components which handle such sensor inputs. The ability to handle such high dimensional signals is due to the explosion of algorithms which use deep neural networks. Sadly, the reason behind the safety issues is also due to deep neural networks themselves. The pitfalls occur due to possible over-fitting, and lack of awareness about the blind spots induced by the training distribution. Ideally, system designers would wish to cover as many scenarios during training as possible. But, achieving a meaningful coverage is impossible. This naturally leads to the following question: Is it feasible to flag out-of-distribution (OOD) samples without causing too many false alarms? Such an OOD detector should be executable in a fashion that is computationally efficient. This is because OOD detectors often are executed as frequently as the sensors are sampled. Our aim in this paper is to build an effective anomaly detector. To this end, we propose the idea of a memory bank to cache data samples which are representative enough to cover most of the in-distribution data. The similarity with respect to such samples can be a measure of familiarity of the test input. This is made possible by an appropriate choice of distance function tailored to the type of sensor we are interested in. Additionally, we adapt conformal anomaly detection framework to capture the distribution shifts with a guarantee of false alarm rate. We report the performance of our technique on two challenging scenarios: a self-driving car setting implemented inside the simulator CARLA with image inputs and autonomous racing car navigation setting with LiDAR inputs. From the experiments, it is clear that a deviation from in-distribution setting can potentially lead to unsafe behavior. Although it should be noted that not all OOD inputs lead to precarious situations in practice, but staying in-distribution is akin to staying within a safety bubble and predictable behavior. An added benefit of our memory based approach is that the OOD detector produces interpretable feedback for a human designer. This is of utmost importance since it recommends a potential fix for the situation as well. In other competing approaches such a feedback is difficult to obtain due to reliance on techniques which use variational autoencoders.

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CODiT: Conformal Out-of-Distribution Detection in Time-Series Data for Cyber-Physical Systems

Cited by 4 publications

References 14 publications

Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling

Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling

Lightning Talk: Trinity - Assured Neuro-symbolic Model Inspired by Hierarchical Predictive Coding

Memory-based Distribution Shift Detection for Learning Enabled Cyber-Physical Systems with Statistical Guarantees

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