30 hp – Conformal Prediction for Counterfactual Generation Introduction Thesis work is an excellent way to get closer to Scania and build relationships for the future. Many of today's employees began their Scania career with their degree project. Background TRATON GROUP is one of the world’s leading commercial vehicle manufacturers, with brands including Scania, MAN, International and Volkswagen Truck & Bus. Its portfolio covers light commercial vehicles, trucks and buses, complemented by financing, charging and digital logistics services. Through its global operations, production sites and extensive sales and service networks, TRATON has access to diverse vehicle platforms, real-world operational data and fleet deployment environments. This provides a strong foundation for developing and validating innovative solutions for sustainable and efficient transportation. In the Cloud and embedded department within TRATON, we develop new solutions for connected vehicles in our Internet of Things IoT platform, as part of TRATON’s increasing focus on communication, services and smart transport solutions. Advanced data analysis capabilities are a cornerstone enabler in this development. Target/scope Modern connected vehicles and industrial systems are increasingly equipped with sensors that continuously monitor their condition. The increasing availability of such data enables data-driven models, for example anomaly detection models, to identify abnormal system behaviour and support monitoring and maintenance decisions. However, many high-performing machine-learning models are inherently difficult to interpret. While a model may identify a sample as anomalous, the prediction itself does not necessarily explain why the sample is considered anomalous or what would need to change for it to be considered normal. Counterfactual explanations provide one approach to addressing this problem. A counterfactual describes how an input could be modified to obtain a different model prediction. However, counterfactuals are commonly generated through optimization, and a generated counterfactual may therefore achieve the desired prediction while being unrealistic or lying far from the distribution represented by real data. Conformal prediction provides a model-agnostic framework for quantifying uncertainty with statistical guarantees and offers an interesting direction for addressing this problem. Recent work has investigated the connection between conformal prediction and counterfactual explanations [1]. The objective of this thesis is to investigate how conformal prediction can be used to validate plausible counterfactual explanations for anomaly detection, and to study under which assumptions meaningful statistical guarantees can be provided. Job description The thesis will investigate methods for generating counterfactual explanations with conformal guarantees. The work will include: Implement a baseline anomaly detection framework: Implement or adapt an existing anom
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