A Mixture-of-Experts Framework for Multi-Stream Quickest Change Detection
Abstract
A Mixture-of-Experts (MoE) framework is proposed for quickest change detection (QCD) where there are multiple experts, and each expert produces a sequence of statistics for change detection. A change is declared when a weighted sum of these statistics crosses a threshold. This framework is then applied to the problem of multi-stream QCD to obtain a family of powerful algorithms that outperform the best known algorithms in the literature. In the multi-stream QCD problem, a decision-maker observes multiple streams of observations. At the change point, the distribution of an unknown subset of the streams changes. The optimal algorithm based on a generalized likelihood ratio is computationally expensive. One of the most computationally and performance-efficient algorithms in the literature is the rank-weighted sum (RWS) scheme based on Shiryaev-Roberts statistics. It is shown in the paper that the MoE-based QCD algorithm for multi-stream data outperforms the RWS scheme. This is demonstrated through simulation results on real-valued Gaussian mean shift data and high-dimensional data from Gaussian--Bernoulli Restricted Boltzmann Machines (GB-RBMs).
Related
- Unlocking Cross-Scenario Physical Layer Security: A Mixture-of-Experts Framework with Generative Diffusion Models
- Mixture of Censored Partially Linear Experts Models for Symmetric or Asymmetric Data with or without Outlying Observations: A novel approach with two-piece scale-mixture of normal distributions
- The Limits of Speculation: Bounding Speculative Decoding in Mixture-of-Experts
- Non-asymptotic oracle inequalities for the Lasso in high-dimensional mixture of experts
- Robust Quickest Change Detection with Cost-Efficient Experiment Design and Unknown Post-Change Model
BibTeX
@misc{chen2026mixtureofexperts,
title = {{A Mixture-of-Experts Framework for Multi-Stream Quickest Change Detection}},
author = {Wuxia Chen and Arman Adibi and Taposh Banerjee and Vahid Tarokh},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.20876},
eprint = {2610.20876},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.20876},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A Mixture-of-Experts Framework for Multi-Stream Quickest Change Detection
AU - Wuxia Chen
AU - Arman Adibi
AU - Taposh Banerjee
AU - Vahid Tarokh
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2610.20876
UR - https://arxiv.org/abs/2610.20876
AB - A Mixture-of-Experts (MoE) framework is proposed for quickest change detection (QCD) where there are multiple experts, and each expert produces a sequence of statistics for change detection. A change is declared when a weighted sum of these statistics crosses a threshold. This framework is then applied to the problem of multi-stream QCD to obtain a family of powerful algorithms that outperform the best known algorithms in the literature. In the multi-stream QCD problem, a decision-maker observes multiple streams of observations. At the change point, the distribution of an unknown subset of the streams changes. The optimal algorithm based on a generalized likelihood ratio is computationally expensive. One of the most computationally and performance-efficient algorithms in the literature is the rank-weighted sum (RWS) scheme based on Shiryaev-Roberts statistics. It is shown in the paper that the MoE-based QCD algorithm for multi-stream data outperforms the RWS scheme. This is demonstrated through simulation results on real-valued Gaussian mean shift data and high-dimensional data from Gaussian--Bernoulli Restricted Boltzmann Machines (GB-RBMs).
ER -