A Bayesian Partially Observable Online Change Detection Approach with Thompson Sampling

References

• Agrawal, S., and Goyal, N. (2012), “Analysis of Thompson Sampling for the Multi-Armed Bandit Problem,” in Conference on Learning Theory, pp. 39–1.
   Google Scholar
• Aitkin, M. (1991), “Posterior Bayes Factors,” Journal of the Royal Statistical Society, Series B, 53, 111–128. DOI: 10.1111/j.2517-6161.1991.tb01812.x.
   Google Scholar
• Allen, G. I., Grosenick, L., and Taylor, J. (2014), “A Generalized Least-Square Matrix Decomposition,” Journal of the American Statistical Association, 109, 145–159. DOI: 10.1080/01621459.2013.852978.
   Web of Science ®Google Scholar
• Ba, S., and Joseph, V. R. (2012), “Composite Gaussian Process Models for Emulating Expensive Functions,” The Annals of Applied Statistics, 6, 1838–1860. DOI: 10.1214/12-AOAS570.
   Web of Science ®Google Scholar
• Ban, Y., and He, J. (2020), “Generic Outlier Detection in Multi-Armed Bandit,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 913–923.
   Google Scholar
• Bubeck, S., Wang, T., and Viswanathan, N. (2013), “Multiple Identifications in Multi-Armed Bandits,” in International Conference on Machine Learning, pp. 258–265.
   Google Scholar
• Cao, Y., Wen, Z., Kveton, B., and Xie, Y. (2019), “Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit,” in The 22nd International Conference on Artificial Intelligence and Statistics, pp. 418–427.
   Google Scholar
• Carbonetto, P., and Stephens, M. (2012), “Scalable Variational Inference for Bayesian Variable Selection in Regression, and its Accuracy in Genetic Association Studies,” Bayesian Analysis, 7, 73–108. DOI: 10.1214/12-BA703.
   Web of Science ®Google Scholar
• Castillo, I., and van der Vaart, A. (2012), “Needles and Straw in a Haystack: Posterior Concentration for Possibly Sparse Sequences,” The Annals of Statistics, 40, 2069–2101. DOI: 10.1214/12-AOS1029.
   Web of Science ®Google Scholar
• Chang, S. I., and Yadama, S. (2010), “Statistical Process Control for Monitoring Non-linear Profiles using Wavelet Filtering and B-spline Approximation,” International Journal of Production Research, 48, 1049–1068.
   Web of Science ®Google Scholar
• Chen, W., Wang, Y., and Yuan, Y. (2013), “Combinatorial Multi-Armed Bandit: General Framework and Applications,” in International Conference on Machine Learning, pp. 151–159.
   Google Scholar
• Chen, W., Wang, Y., Yuan, Y., and Wang, Q. (2016), “Combinatorial Multi-Armed Bandit and its Extension to Probabilistically Triggered Arms,” The Journal of Machine Learning Research, 17, 1746–1778.
   Web of Science ®Google Scholar
• Durand, A., and Gagné, C. (2014), “Thompson Sampling for Combinatorial Bandits and its Application to Online Feature Selection,” in Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence.
   Google Scholar
• George, E. I., and McCulloch, R. E. (1993), “Variable Selection via Gibbs Sampling,” Journal of the American Statistical Association, 88, 881–889. DOI: 10.1080/01621459.1993.10476353.
   Web of Science ®Google Scholar
• George, E. I. (1997), “Approaches for Bayesian Variable Selection,” Statistica Sinica, 7, 339–373.
   Web of Science ®Google Scholar
• Ghatak, G. (2020), “A Change-Detection Based Thompson Sampling Framework for Non-Stationary Bandits,” IEEE Transactions on Computers, 70, 1670–1676. DOI: 10.1109/TC.2020.3022634.
   Web of Science ®Google Scholar
• Guan, Y., and Stephens, M. (2011), “Bayesian Variable Selection Regression for Genome-Wide Association Studies and other Large-Scale Problems,” The Annals of Applied Statistics, 5, 1780–1815. DOI: 10.1214/11-AOAS455.
   Web of Science ®Google Scholar
• Hyvärinen, A., and Oja, E. (2000), “Independent Component Analysis: Algorithms and Applications,” Neural Networks, 13, 411–430. DOI: 10.1016/S0893-6080(00)00026-5.
   PubMed Web of Science ®Google Scholar
• Ishwaran, H., and Rao, J. S. (2005), “Spike and Slab Variable Selection: Frequentist and Bayesian Strategies,” The Annals of Statistics, 33, 730–773. DOI: 10.1214/009053604000001147.
   Web of Science ®Google Scholar
• Kalyanakrishnan, S., Tewari, A., Auer, P., and Stone, P. (2012), “PAC Subset Selection in Stochastic Multi-Armed Bandits,” in ICML (Vol. 12), pp. 655–662.
   Google Scholar
• Liu, F., Lee, J., and Shroff, N. (2018), “A Change-Detection based Framework for Piecewise-Stationary Multi-Armed Bandit Problem,” in Thirty-Second AAAI Conference on Artificial Intelligence. DOI: 10.1609/aaai.v32i1.11746.
   Google Scholar
• Liu, K., Mei, Y., and Shi, J. (2015), “An Adaptive Sampling Strategy for Online High-Dimensional Process Monitoring,” Technometrics, 57, 305–319. DOI: 10.1080/00401706.2014.947005.
   Web of Science ®Google Scholar
• Liu, K., and Shi, J. (2013), “Objective-Oriented Optimal Sensor Allocation Strategy for Process Monitoring and Diagnosis by Multivariate Analysis in a Bayesian Network,” IIE Transactions, 45, 630–643. DOI: 10.1080/0740817X.2012.725505.
   Web of Science ®Google Scholar
• Liu, Y., and Rocková, V. (2020), “Variable Selection via Thompson Sampling,” arXiv preprint arXiv:2007.00187.
   Google Scholar
• Mairal, J., Bach, F., Ponce, J., Sapiro, G., and Zisserman, A. (2008), “Supervised Dictionary Learning,” arXiv preprint arXiv:0809.3083.
   Google Scholar
• Mei, Y. (2010), “Efficient Scalable Schemes for Monitoring a Large Number of Data Streams,” Biometrika, 97, 419–433. DOI: 10.1093/biomet/asq010.
   Web of Science ®Google Scholar
• Meier, L., Van de Geer, S., and Bühlmann, P. (2009), “High-Dimensional Additive Modeling,” The Annals of Statistics, 37, 3779–3821. DOI: 10.1214/09-AOS692.
   Web of Science ®Google Scholar
• Mellor, J., and Shapiro, J. (2013), “Thompson Sampling in Switching Environments with Bayesian Online Change Detection,” in Artificial Intelligence and Statistics, pp. 442–450, PMLR.
   Google Scholar
• Mo, X., Monga, V., Bala, R., and Fan, Z. (2013), “Adaptive Sparse Representations for Video Anomaly Detection,” IEEE Transactions on Circuits and Systems for Video Technology, 24, 631–645.
   Web of Science ®Google Scholar
• Montgomery, D. C. (2020), Introduction to Statistical Quality Control, New York: Wiley.
   Google Scholar
• Ramsay, J., and Silverman, B. (2005), “Principal Components Analysis for Functional Data,” in Functional Data Analysis, 147–172, New York: Springer.
   Google Scholar
• Ročková, V. (2018), “Bayesian Estimation of Sparse Signals with a Continuous Spike-and-Slab Prior,” The Annals of Statistics, 46, 401–437. DOI: 10.1214/17-AOS1554.
   Web of Science ®Google Scholar
• Ročková, V., and George, E. I. (2014), “EMVS: The EM Approach to Bayesian Variable Selection,” Journal of the American Statistical Association, 109, 828–846. DOI: 10.1080/01621459.2013.869223.
   Web of Science ®Google Scholar
• Rockova, V., and McAlinn, K. (2021), “Dynamic Variable Selection with Spike-and-Slab Process Priors,” Bayesian Analysis, 16, 233–269. DOI: 10.1214/20-BA1199.
   Web of Science ®Google Scholar
• Wang, A., Xian, X., Tsung, F., and Liu, K. (2018a), “A Spatial-Adaptive Sampling Procedure for Online Monitoring of Big Data Streams,” Journal of Quality Technology, 50, 329–343. DOI: 10.1080/00224065.2018.1507560.
   Web of Science ®Google Scholar
• Wang, H., Sievert, S., Liu, S., Charles, Z., Papailiopoulos, D., and Wright, S. (2018b), “Atomo: Communication-Efficient Learning via Atomic Sparsification,” in Advances in Neural Information Processing Systems, pp. 9850–9861.
   Google Scholar
• Wang, K., and Jiang, W. (2009), “High-Dimensional Process Monitoring and Fault Isolation via Variable Selection,” Journal of Quality Technology, 41, 247–258. DOI: 10.1080/00224065.2009.11917780.
   Web of Science ®Google Scholar
• Wang, Y., and Blei, D. M. (2019), “Frequentist Consistency of Variational Bayes,” Journal of the American Statistical Association, 114, 1147–1161. DOI: 10.1080/01621459.2018.1473776.
   Web of Science ®Google Scholar
• Wood, S. N., Goude, Y., and Shaw, S. (2015), “Generalized Additive Models for Large Data Sets,” Journal of the Royal Statistical Society, Series C, 64, 139–155. DOI: 10.1111/rssc.12068.
   Web of Science ®Google Scholar
• Xian, X., Wang, A., and Liu, K. (2017), “A Nonparametric Adaptive Sampling Strategy for Online Monitoring of Big Data Streams,” Technometrics, 60, 14–25. DOI: 10.1080/00401706.2017.1317291.
   Web of Science ®Google Scholar
• Xian, X., Zhang, C., Bonk, S., and Liu, K. (2019), “Online Monitoring of Big Data Streams: A Rank-based Sampling Algorithm by Data Augmentation,” Journal of Quality Technology, 53, 135–153. DOI: 10.1080/00224065.2019.1681924.
   Web of Science ®Google Scholar
• Xu, Y., Wu, Z., and Wei, Z. (2015), “Spectral–Spatial Classification of Hyperspectral Image based on Low-Rank Decomposition,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2370–2380. DOI: 10.1109/JSTARS.2015.2434997.
   Web of Science ®Google Scholar
• Yan, H., Paynabar, K., and Shi, J. (2017), “Anomaly Detection in Images with Smooth Background via Smooth-Sparse Decomposition,” Technometrics, 59, 102–114. DOI: 10.1080/00401706.2015.1102764.
   Web of Science ®Google Scholar
• Yan, H. (2018), “Real-Time Monitoring of High-Dimensional Functional Data Streams via Spatio-Temporal Smooth Sparse Decomposition,” Technometrics, 60, 181–197.
   Web of Science ®Google Scholar
• Zhang, C., Chen, N., and Wu, J. (2020), “Spatial Rank-based High-Dimensional Monitoring through Random Projection,” Journal of Quality Technology, 52, 111–127. DOI: 10.1080/00224065.2019.1571336.
   Web of Science ®Google Scholar
• Zhang, C., and Hoi, S. C. (2019), “Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach,” in Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33), pp. 5733–5740. DOI: 10.1609/aaai.v33i01.33015733.
   Google Scholar
• Zhang, C., Yan, H., Lee, S., and Shi, J. (2018), “Weakly Correlated Profile Monitoring based on Sparse Multi-Channel Functional Principal Component Analysis,” IISE Transactions, 50, 878–891. DOI: 10.1080/24725854.2018.1451012.
   Web of Science ®Google Scholar
• Zhang, C. (2021), “Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning,” Technometrics, 63, 370–383.
   Web of Science ®Google Scholar
• Zhang, L., Wang, K., and Chen, N. (2016), “Monitoring Wafers’ Geometric Quality using an Additive Gaussian Process Model,” IIE Transactions, 48, 1–15. DOI: 10.1080/0740817X.2015.1027455.
   Web of Science ®Google Scholar
• Zhang, W., and Mei, Y. (2022), “Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data under Sampling Control,” Technometrics. DOI: 10.1080/00401706.2022.2054861..
   PubMed Web of Science ®Google Scholar
• Zhuang, H., Wang, C., and Wang, Y. (2017), “Identifying Outlier Arms in Multi-Armed Bandit,” in Advances in Neural Information Processing Systems, pp. 5204–5213.
   Google Scholar
• Zou, C., and Qiu, P. (2009), “Multivariate Statistical Process Control using LASSO,” Journal of the American Statistical Association, 104, 1586–1596. DOI: 10.1198/jasa.2009.tm08128.
   Web of Science ®Google Scholar