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Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program)., , , , , , , and . J. Mach. Learn. Res., (2021)Stochastic Gradient MCMC for Nonlinear State Space Models., , , , and . CoRR, (2019)Approximate Collapsed Gibbs Clustering with Expectation Propagation., and . CoRR, (2018)Streaming Variational Inference for Bayesian Nonparametric Mixture Models., , and . AISTATS, volume 38 of JMLR Workshop and Conference Proceedings, JMLR.org, (2015)Sharing Features among Dynamical Systems with Beta Processes., , , and . NIPS, page 549-557. Curran Associates, Inc., (2009)Large-Scale Stochastic Sampling from the Probability Simplex., , , and . NeurIPS, page 6722-6732. (2018)Adaptively Truncating Backpropagation Through Time to Control Gradient Bias., , and . UAI, volume 115 of Proceedings of Machine Learning Research, page 799-808. AUAI Press, (2019)Representing documents through their readers., , , and . KDD, page 14-22. ACM, (2013)Hierarchical Dirichlet processes for tracking maneuvering targets., , and . FUSION, page 1-8. IEEE, (2007)Bayesian nonparametric models of sparse and exchangeable random graphs., and . CoRR, (2014)