Thompson Sampling-Based Partially Observable Online Change Detection for Exponential Families

References

• Agrawal S, Goyal N (2012) Analysis of Thompson sampling for the multi-armed bandit problem. 25th Annu. Conf. Learn. Theory (PMLR, New York), 39.1–39.26.Google Scholar
• Ban Y, He J (2020) Generic outlier detection in multi-armed bandit. Proc. 26th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 913–923.Google Scholar
• Bubeck S, Wang T, Viswanathan N (2013) Multiple identifications in multi-armed bandits. 30th Internat. Conf. Machine Learning (JMLR, Norfolk, MA), 258–265.Google Scholar
• Cao Y, Wen Z, Kveton B, Xie Y (2019) Nearly optimal adaptive procedure with change detection for piecewise-stationary bandit. 22nd Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 418–427.Google Scholar
• Cavenaghi E, Sottocornola G, Stella F, Zanker M (2021) Non stationary multi-armed bandit: Empirical evaluation of a new concept drift-aware algorithm. Entropy 23(3):380.Google Scholar
• Chen W, Wang Y, Yuan Y (2013) Combinatorial multi-armed bandit: General framework and applications. Internat. Conf. Machine Learn. (JMLR, Norfolk, MA), 151–159.Google Scholar
• Chen W, Wang Y, Yuan Y, Wang Q (2016) Combinatorial multi-armed bandit and its extension to probabilistically triggered arms. J. Machine Learn. Res. 17(1):1746–1778.Google Scholar
• Chiquet J, Mariadassou M, Robin S (2018) Variational inference for probabilistic Poisson PCA. Ann. Appl. Statist. 12(4):2674–2698.Google Scholar
• Collins M, Dasgupta S, Schapire RE (2001) A generalization of principal components analysis to the exponential family. Adv. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 617–624.Google Scholar
• Durand A, Gagné C (2014) Thompson sampling for combinatorial bandits and its application to online feature selection. Workshops Twenty-Eighth AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA).Google Scholar
• George EI, McCulloch RE (1997) Approaches for Bayesian variable selection. Statist. Sinica 7(2):339–373.Google Scholar
• Ghatak G (2020) A change-detection-based Thompson sampling framework for non-stationary bandits. IEEE Trans. Comput. 70(10):1670–1676.Google Scholar
• Gómez AME, Li D, Paynabar K (2022) An adaptive sampling strategy for online monitoring and diagnosis of high-dimensional streaming data. Technometrics 64(2):253–269.Google Scholar
• Hardin JW, Hilbe JW (2012) Generalized Linear Models and Extensions (Stata Press Books, College Station, TX).Google Scholar
• Ishwaran H, Rao JS (2005) Spike and slab variable selection: Frequentist and Bayesian strategies. Ann. Statist. 33(2):730–773.Google Scholar
• Jun KS, Jamieson K, Nowak R, Zhu X (2016) Top arm identification in multi-armed bandits with batch arm pulls. Proc. 19th Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 139–148.Google Scholar
• Kaufmann E, Korda N, Munos R (2012) Thompson sampling: An asymptotically optimal finite-time analysis. Bshouty NH, Stoltz G, Vayatis N, Zeugmann T, eds. Internat. Conf. Algorithmic Learn. Theory (Springer, Berlin, Heidelberg), 199–213.Google Scholar
• Li G, Huang JZ, Shen H (2018a) Exponential family functional data analysis via a low-rank model. Biometrics 74(4):1301–1310.Google Scholar
• Li J, Zhang J, Pang N, Qin X (2018b) Weighted outlier detection of high-dimensional categorical data using feature grouping. IEEE Trans. Systems Man Cybernetics Systems 50(11):4295–4308.Google Scholar
• Liu K, Mei Y, Shi J (2015) An adaptive sampling strategy for online high-dimensional process monitoring. Technometrics 57(3):305–319.Google Scholar
• Lu X, Yuan Y, Zheng X (2016) Joint dictionary learning for multispectral change detection. IEEE Trans. Cybernetics 47(4):884–897.Google Scholar
• Mei Y (2010) Efficient scalable schemes for monitoring a large number of data streams. Biometrika 97(2):419–433.Google Scholar
• Meier L, Van de Geer S, Bühlmann P (2009) High-dimensional additive modeling. Ann. Statist. 37(6B):3779–3821.Google Scholar
• Montgomery DC (2007) Introduction to Statistical Quality Control (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Mukherjee A, Marozzi M (2021) Nonparametric phase-II control charts for monitoring high-dimensional processes with unknown parameters. J. Quality Tech. 54(1):44–64.Google Scholar
• Petterson J, Yu J, McAuley J, Caetano T (2009) Exponential family graph matching and ranking. Adv. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 1455–1463.Google Scholar
• Qiu P, Zou C, Wang Z (2010) Nonparametric profile monitoring by mixed effects modeling. Technometrics 52(3):265–277.Google Scholar
• Ročková V (2018) Bayesian estimation of sparse signals with a continuous spike-and-slab prior. Ann. Statist. 46(1):401–437.Google Scholar
• Russo DJ, Van Roy B, Kazerouni A, Osband I, Wen Z (2018) A tutorial on Thompson sampling. Foundations Trends® Machine Learn. 11(1):1–96.Google Scholar
• Tartakovsky A (2019) Sequential Change Detection and Hypothesis Testing: General Non-iid Stochastic Models and Asymptotically Optimal Rules (CRC Press, Boca Raton, FL).Google Scholar
• Thompson WR (1933) On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25(3–4):285–294.Google Scholar
• Wang Y, Blei DM (2019) Frequentist consistency of variational bayes. J. Amer. Statist. Assoc. 114(527):1147–1161.Google Scholar
• Wang K, Jiang W (2009) High-dimensional process monitoring and fault isolation via variable selection. J. Quality Tech. 41(3):247–258.Google Scholar
• Wang A, Xian X, Tsung F, Liu K (2018) A spatial-adaptive sampling procedure for online monitoring of big data streams. J. Quality Tech. 50(4):329–343.Google Scholar
• Xian X, Wang A, Liu K (2018) A nonparametric adaptive sampling strategy for online monitoring of big data streams. Technometrics 60(1):14–25.Google Scholar
• Xian X, Zhang C, Bonk S, Liu K (2021) Online monitoring of big data streams: A rank-based sampling algorithm by data augmentation. J. Quality Tech. 53(2):135–153.Google Scholar
• Xie Y, Huang J, Willett R (2012) Change-point detection for high-dimensional time series with missing data. IEEE J. Selected Topics Signal Processing 7(1):12–27.Google Scholar
• Yan H, Paynabar K, Shi J (2014) Image-based process monitoring using low-rank tensor decomposition. IEEE Trans. Automation Sci. Engrg. 12(1):216–227.Google Scholar
• Yan H, Paynabar K, Shi J (2017) Anomaly detection in images with smooth background via smooth-sparse decomposition. Technometrics 59(1):102–114.Google Scholar
• Yan H, Paynabar K, Shi J (2018) Real-time monitoring of high-dimensional functional data streams via spatio-temporal smooth sparse decomposition. Technometrics 60(2):181–197.Google Scholar
• Zhang C, Hoi SC (2019) Partially observable multi-sensor sequential change detection: A combinatorial multi-armed bandit approach. Proc. Conf. AAAI Artificial Intelligence, vol. 33, 5733–5740.Google Scholar
• Zhang W, Mei Y (2020) Bandit change-point detection for real-time monitoring high-dimensional data under sampling control. Preprint, submitted September 24, https://arxiv.org/abs/2009.11891.Google Scholar
• Zhang C, Chen N, Wu J (2020) Spatial rank-based high-dimensional monitoring through random projection. J. Quality Tech. 52(2):111–127.Google Scholar
• Zhang L, Wang K, Chen N (2016) Monitoring wafers’ geometric quality using an additive Gaussian process model. IIE Trans. 48(1):1–15.Google Scholar
• Zhang C, Yan H, Lee S, Shi J (2018) Weakly correlated profile monitoring based on sparse multi-channel functional principal component analysis. IISE Trans. 50(10):878–891.Google Scholar
• Zhou H, Wang L, Varshney L, Lim EP (2020) A near-optimal change-detection based algorithm for piecewise-stationary combinatorial semi-bandits. Proc. Conf. AAAI Artificial Intelligence, vol. 34, 6933–6940.Google Scholar
• Zhuang H, Wang C, Wang Y (2017) Identifying outlier arms in multi-armed bandit. Adv. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 5204–5213.Google Scholar
• Zou C, Qiu P (2009) Multivariate statistical process control using lasso. J. Amer. Statist. Assoc. 104(488):1586–1596.Google Scholar