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When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of learned components in the modern robot autonomy stack.
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Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Safe visual navigation via deep learning and novelty detection
Charles Richter and Nicholas Roy · 2017
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CyCADA: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei Efros, and Trevor Darrell · 2018
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A general safety framework for learning-based control in uncertain robotic systems
Jaime F. Fisac, Anayo K. Akametalu, Melanie N. Zeilinger, Shahab Kaynama, Jeremy Gillula, and Claire J. Tomlin · 2019
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Learning to adapt in dynamic, real-world environments through meta-reinforcement learning
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Mohammadreza Salehi, Hossein Mirzaei, Dan Hendrycks, Yixuan Li, Mohammad Hossein Rohban, and Mohammad Sabokrou · 2021
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A unifying review of deep and shallow anomaly detection
Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, and Klaus-Robert Müller · 2021
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Generalized out-of-distribution detection: A survey
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Wilds: A benchmark of in-the-wild distribution shifts
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Sketching curvature for efficient out-of-distribution detection for deep neural networks
Apoorva Sharma, Navid Azizan, and Marco Pavone · 2021
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Distributionally robust neural networks
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A survey of unsupervised deep domain adaptation
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On infusing reachability-based safety assurance within planning frameworks for human-robot vehicle interactions
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A survey of generalisation in deep reinforcement learning
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Domain generalization: A survey
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Openood v1.5: Enhanced benchmark for out-of-distribution detection
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