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This research establishes a theoretical framework for out-of-distribution detection in dynamic environments by integrating reinforcement learning optimizers into standard gradient descent processes. The study demonstrates how this approach improves future-domain generalization and reduces semantic OOD false positives over time, addressing critical challenges for AI systems operating in evolving real-world scenarios. By explicitly accounting for post-deployment environment changes, the work advances the robustness of machine learning models against distribution shifts without relying on hype or consumer-focused narratives.
Theoretical Grounding of Out-Of-Distribution Detection With Reinforcement Learning Optimizer
arXiv:2606.17477v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection in dynamic open-world environments requires a model to continually adapt to evolving data distributions while generalizing to covariate-shifted inputs and rejecting semantic-shifted OOD examples. Most existing OOD detection methods optimize only the current-step objective and do not explicitly account for how post-deployment environment changes affect future OOD behavior. In this paper, we establish a theoretical grounding for dynamic OOD detection using a reinforcement learning (RL)-guided optimizer that explicitly favors updates that reduce the semantic OOD false positive rate over time. We develop a novel augmented optimizer that uses an RL-guided correction term on top of standard gradient descent (GD) and show its improvement over both future-domain generalization and semantic-OOD rejection. We analyze temporal error decomposition in terms of model-change and environment-change generalization errors and develop a new theoretical framework for comparing the generalization errors under both GD and RL-guided optimizers.
Abstract:Out-of-distribution (OOD) detection in dynamic open-world environments requires a model to continually adapt to evolving data distributions while generalizing to covariate-shifted inputs and rejecting semantic-shifted OOD examples. Most existing OOD detection methods optimize only the current-step objective and do not explicitly account for how post-deployment environment changes affect future OOD behavior. In this paper, we establish a theoretical grounding for dynamic OOD detection using a reinforcement learning (RL)-guided optimizer that explicitly favors updates that reduce the semantic OOD false positive rate over time. We develop a novel augmented optimizer that uses an RL-guided correction term on top of standard gradient descent (GD) and show its improvement over both future-domain generalization and semantic-OOD rejection. We analyze temporal error decomposition in terms of model-change and environment-change generalization errors and develop a new theoretical framework for comparing the generalization errors under both GD and RL-guided optimizers.
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
arXiv:2606.17477 [cs.CV]
(or arXiv:2606.17477v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.17477 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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