Online Modeling and Monitoring of Dependent Processes under Resource Constraints

• URL: http://arxiv.org/abs/2307.14208v2
• Date: Sat, 21 Oct 2023 23:14:34 GMT
• Title: Online Modeling and Monitoring of Dependent Processes under Resource Constraints
• Authors: Tanapol Kosolwattana, Huazheng Wang, Ying Lin
• Abstract summary: The proposed method designs a collaborative learning-based upper confidence bound (CL-UCB) algorithm to optimally balance the exploitation and exploration of dependent processes under limited resources. efficiency of the proposed method is demonstrated through theoretical analysis, simulation studies and an empirical study of adaptive cognitive monitoring in Alzheimer's disease.
• Score: 11.813520177037763
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive monitoring of a large population of dynamic processes is critical for the timely detection of abnormal events under limited resources in many healthcare and engineering systems. Examples include the risk-based disease screening and condition-based process monitoring. However, existing adaptive monitoring models either ignore the dependency among processes or overlook the uncertainty in process modeling. To design an optimal monitoring strategy that accurately monitors the processes with poor health conditions and actively collects information for uncertainty reduction, a novel online collaborative learning method is proposed in this study. The proposed method designs a collaborative learning-based upper confidence bound (CL-UCB) algorithm to optimally balance the exploitation and exploration of dependent processes under limited resources. Efficiency of the proposed method is demonstrated through theoretical analysis, simulation studies and an empirical study of adaptive cognitive monitoring in Alzheimer's disease.

Related papers

• Model-Free Active Exploration in Reinforcement Learning [53.786439742572995]
  We study the problem of exploration in Reinforcement Learning and present a novel model-free solution. Our strategy is able to identify efficient policies faster than state-of-the-art exploration approaches.
  arXiv  Detail & Related papers  (2024-06-30T19:00:49Z)
• FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning [11.55303447173952]
  We propose a novel collaborative UCB algorithm to estimate the representative models from sequentially observed decentralized data. The efficiency of our method is illustrated through theoretical analysis, simulation studies, and decentralized cognitive degradation monitoring in Alzheimer's disease.
  arXiv  Detail & Related papers  (2024-05-30T21:49:14Z)
• Decentralized Event-Triggered Online Learning for Safe Consensus of Multi-Agent Systems with Gaussian Process Regression [3.405252606286664]
  This paper presents a novel learning-based distributed control law, augmented by an auxiliary dynamics. For continuous enhancement in predictive performance, a data-efficient online learning strategy with a decentralized event-triggered mechanism is proposed. To demonstrate the efficacy of the proposed learning-based controller, a comparative analysis is conducted, contrasting it with both conventional distributed control laws and offline learning methodologies.
  arXiv  Detail & Related papers  (2024-02-05T16:41:17Z)
• Mining a Minimal Set of Behavioral Patterns using Incremental Evaluation [3.16536213610547]
  Existing approaches to behavioral pattern mining suffer from two limitations. First, they show limited scalability as incremental computation is incorporated only in the generation of pattern candidates. Second, process analysis based on mined patterns shows limited effectiveness due to an overwhelmingly large number of patterns obtained in practical application scenarios.
  arXiv  Detail & Related papers  (2024-02-05T11:41:37Z)
• Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens [6.329470650220206]
  The aim of this work is to highlight the relatively under-appreciated complexity of designing a monitoring strategy. We consider an ML-based risk prediction algorithm for predicting unplanned readmissions. Results from this case study emphasize the seemingly simple (and obvious) fact that not all monitoring systems are created equal.
  arXiv  Detail & Related papers  (2023-11-20T00:15:16Z)
• Distributionally Robust Model-based Reinforcement Learning with Large State Spaces [55.14361269378122]
  Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment deployment. We study distributionally robust Markov decision processes with continuous state spaces under the widely used Kullback-Leibler, chi-square, and total variation uncertainty sets. We propose a model-based approach that utilizes Gaussian Processes and the maximum variance reduction algorithm to efficiently learn multi-output nominal transition dynamics.
  arXiv  Detail & Related papers  (2023-09-05T13:42:11Z)
• Extending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes [62.997667081978825]
  The paper presents an approach to process mining providing semi-automatic support to model optimization. A model simplification approach is proposed, which essentially abstracts the raw model at the desired granularity. We aim to demonstrate the capabilities of the technological solution using three datasets from different applications in the healthcare domain.
  arXiv  Detail & Related papers  (2022-06-10T16:20:59Z)
• Prescriptive Process Monitoring: Quo Vadis? [64.39761523935613]
  The paper studies existing methods in this field via a Systematic Literature Review ( SLR) The SLR provides insights into challenges and areas for future research that could enhance the usefulness and applicability of prescriptive process monitoring methods.
  arXiv  Detail & Related papers  (2021-12-03T08:06:24Z)
• DEALIO: Data-Efficient Adversarial Learning for Imitation from Observation [57.358212277226315]
  In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator. Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms. This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk. We propose a more data-efficient IfO algorithm
  arXiv  Detail & Related papers  (2021-03-31T23:46:32Z)
• SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
  SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
  arXiv  Detail & Related papers  (2020-06-12T10:40:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.