Page 160 - 《软件学报》2026年第2期
P. 160

王浩天 等: 扩散模型引导的根因分析                                                               639


                     Proc. of the 37th Int’l Conf. Neural Information Processing Systems. New Orleans: Curran Associates Inc., 2023. 1935.
                 [21]   Stein C. A bound for the error in the normal approximation to the distribution of a sum of dependent random variables. In: Proc. of the 6th
                     Berkeley Symp. Mathematical Statistics and Probability. Berkeley: University of California Press, 1972. 583–602.
                 [22]   Li  YZ,  Turner  RE.  Gradient  estimators  for  implicit  models.  In:  Proc.  of  the  6th  Int’l  Conf.  Learning  Representations.  Vancouver:
                     OpenReview.net, 2018.
                 [23]   Cheng  W,  Zhang  K,  Chen  HF,  Jiang  GF,  Chen  ZZ,  Wang  W.  Ranking  causal  anomalies  via  temporal  and  dynamical  analysis  on
                     vanishing correlations. In: Proc. of the 22nd ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining. San Francisco: ACM,
                     2016. 805–814. [doi: 10.1145/2939672.2939765]
                 [24]   Capozzoli  A,  Lauro  F,  Khan  I.  Fault  detection  analysis  using  data  mining  techniques  for  a  cluster  of  smart  office  buildings.  Expert
                     Systems with Applications, 2015, 42(9): 4324–4338. [doi: 10.1016/j.eswa.2015.01.010]
                 [25]   Duan PT, He ZZ, He YH, Liu FD, Zhang AQ, Zhou D. Root cause analysis approach based on reverse cascading decomposition in QFD
                     and fuzzy weight ARM for quality accidents. Computers & Industrial Engineering, 2020, 147: 106643. [doi: 10.1016/j.cie.2020.106643]
                 [26]   Meng Y, Zhang SL, Sun YQ, Zhang RR, Hu ZL, Zhang YY, Jia CY, Wang ZG, Pei D. Localizing failure root causes in a microservice
                     through causality inference. In: Proc. of the 28th IEEE/ACM Int’l Symp. Quality of Service (IWQoS). Hangzhou: IEEE, 2020. 1–10.
                     [doi: 10.1109/IWQoS49365.2020.9213058]
                 [27]   Soldani  J,  Brogi  A.  Anomaly  detection  and  failure  root  cause  analysis  in  (micro)  service-based  cloud  applications:  A  survey.  ACM
                     Computing Surveys (CSUR), 2022, 55(3): 59. [doi: 10.1145/3501297]
                 [28]   Shimizu S. Lingam: Non-Gaussian methods for estimating causal structures. Behaviormetrika, 2014, 41(1): 65–98. [doi: 10.2333/bhmk.
                     41.65]
                 [29]   Wang YH, Solus L, Yang KD, Uhler C. Permutation-based causal inference algorithms with interventions. In: Proc. of the 31st Int’l Conf.
                     Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017. 5824–5833.
                 [30]   Yang YQ, Salehkaleybar S, Kiyavash N. Learning unknown intervention targets in structural causal models from heterogeneous data. In:
                     Proc. of the 27th Int’l Conf. Artificial Intelligence and Statistics. Valencia: PMLR, 2024. 3187–3195.
                 [31]   Hoyer PO, Janzing D, Mooij J, Peters J, Schölkopf B. Nonlinear causal discovery with additive noise models. In: Proc. of the 22nd Int’l
                     Conf. Neural Information Processing Systems. Vancouver: Curran Associates Inc., 2008. 689–696.
                 [32]   Peters J, Mooij JM, Janzing D, Schölkopf B. Causal discovery with continuous additive noise models. The Journal of Machine Learning
                     Research, 2014, 15(1): 2009–2053.
                 [33]   Rolland  P,  Cevher  V,  Kleindessner  M,  Russel  C,  Janzing  D,  Schölkopf  B,  Locatello  F.  Score  matching  enables  causal  discovery  of
                     nonlinear additive noise models. In: Proc. of the 39th Int’l Conf. Machine Learning. Baltimore: PMLR, 2022. 18741–18753.
                 [34]   Sohl-Dickstein J, Weiss E, Maheswaranathan N, Ganguli S. Deep unsupervised learning using nonequilibrium thermodynamics. In: Proc.
                     of the 32nd Int’l Conf. Machine Learning. Lille: PMLR, 2015. 2256–2265.
                 [35]   Kingma DP, Salimans T, Poole B, Ho J. Variational diffusion models. In: Proc. of the 35th Int’l Conf. Neural Information Processing
                     Systems. Curran Associates Inc., 2021. 1660.
                 [36]   Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. In: Proc. of the 34th Int’l Conf. Neural Information Processing Systems.
                     Vancouver: Curran Associates Inc., 2020. 574.
                 [37]   Song Y, Sohl-Dickstein J, Kingma DP, Kumar A, Ermon S, Poole B. Score-based generative modeling through stochastic differential
                     equations. In: Proc. of the 9th Int’l Conf. Learning Representation. OpenReview.net, 2021.
                 [38]   Ramzi  Z,  Remy  B,  Lanusse  F,  Starck  JL,  Ciuciu  P.  Denoising  score-matching  for  uncertainty  quantification  in  inverse  problems.
                     arXiv:2011.08698, 2020.
                 [39]   Ma  JM,  Li  XH,  Wang  ZH,  Zhang  XC,  Yan  SG,  Chen  YT,  Zhang  YQ,  Jin  MX,  Jiang  LJ,  Liang  Y,  Yang  C,  Lin  DH.  A  holistic
                     functionalization  approach  to  optimizing  imperative  tensor  programs  in  deep  learning.  In:  Proc.  of  the  61st  ACM/IEEE  Design
                     Automation Conf. San Francisco: ACM, 2024. 200. [doi: 10.1145/3649329.3658483]
                 [40]   Lachapelle  S,  Brouillard  P,  Deleu  T,  Lacoste-Julien  S.  Gradient-based  neural  dag  learning.  In:  Proc.  of  the  8th  Int’l  Conf.  Learning
                     Representations. Addis Ababa: OpenReview.net, 2020.
                 [41]   Mathur AP, Tippenhauer NO. SWaT: A water treatment testbed for research and training on ICS security. In: Proc. of the 2016 Int’l
                     Workshop on Cyber-physical Systems for Smart Water Networks (CySWater). Vienna: IEEE, 2016. 31–36. [doi: 10.1109/CySWater.
                     2016.7469060]
                 [42]   Shan HS, Chen Y, Liu HF, Zhang YP, Xiao X, He XF, Li M, Ding W. ϵ-Diagnosis: Unsupervised and real-time diagnosis of small-
                     window long-tail latency in large-scale microservice platforms. In: Proc. of the 2019 World Wide Web Conf. San Francisco: ACM, 2019.
                     3215–3222. [doi: 10.1145/3308558.3313653]
   155   156   157   158   159   160   161   162   163   164   165