Quickest change detection with unknown parameters: Constant complexity and near optimality
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Abstract (arXiv)
We consider the quickest change detection problem where both the parameters of pre- and post- change distributions are unknown, which prevents the use of classical simple hypothesis testing. Without additional assumptions, optimal solutions are not tractable as they rely on some minimax and robust variant of the objective. As a consequence, change points might be detected too late for practical applications (in economics, health care or maintenance for instance). Available constant complexity techniques typically solve a relaxed version of the problem, deeply relying on very specific probability distributions and/or some very precise additional knowledge. We consider a totally different approach that leverages the theoretical asymptotic properties of optimal solutions to derive a new scalable approximate algorithm with near optimal performance that runs~in~O(1), adapted to even more complex Markovian settings.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 44 references that are in the map. First posted on arXiv 9 Jun 2021.
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References · 9
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2016Generative Adversarial Imitation Learningcited by 1,576
- 2017Hindsight Experience Replaycited by 1,096
- 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 256
- 2017Distral: Robust Multitask Reinforcement Learningcited by 231
- 2018Meta-Reinforcement Learning of Structured Exploration Strategiescited by 128
- 2019Meta-Inverse Reinforcement Learning with Probabilistic Context Variablescited by 50
- 2018Multi-task Maximum Entropy Inverse Reinforcement Learningcited by 20
- 2013Sequential Change Detection in the Presence of Unknown Parameterscited by 1
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