Workshops Organization:
Integration of Deep Learning Theories at NIPS 2018, Palais des Congrès de Montréal, Canada.
— Coorganize with Professor Richard Baraniuk, Stephane Mallat, Anima Anandkumar, and Ankit Patel.
Conferences, Seminars, and Workshops Presentations:
On multilayer latent variable models: Computational and statistical perspectives. Mathematics of Data and Decisions Seminar, Department of Mathematics, UC Davis, 2019.
On optimal transport in machine learning and statistics: Computational, modeling, and theoretical perspectives. Research seminar, VinAI Research, Ha Noi, 2019.
Statistical and computational perspective of mixture and hierarchical models. BLISS Seminar, Department of EECS, UC Berkeley, 2019.
Singularity structures of mixture models: Statistical and computational perspective. Joint Statistical Meetings (JSM), Denver, Colorado, 2019.
On efficient optimal transport: an analysis of greedy and accelerated mirror descent algorithms. International Conference on Machine Learning (ICML), Long Beach, CA, 2019.
Singularity structures of mixture models: Statistical and computational perspective. Department
Seminar, Department of Electrical Engineering and Computer Sciences, Rice, November, 2018,
Houston, Texas.
Singularity structures of parameter estimation in nite mixtures of distributions. Joint Stanford and Berkeley Applied Math Event, November 2018, University of California, Berkeley.
Singularity Structure of Parameter Space and Posterior Contraction in Finite Mixture Models. Joint Statistical Meetings (JSM), August, 2017, Baltimore, Maryland.
Singularity structures and parameter estimation behavior in finite mixtures of distributions. Nonparametric Statistics Workshop: Integration of Theory, Methods, and Applications, October, 2016, Ann Arbor, Michigan.
Singularity structures and impacts on parameter estimation in finite mixtures of distributions. Shannon Centennial Symposium, September, 2016, Ann Arbor, Michigan.
Singularity structures and parameter estimation behavior in finite mixtures of distributions. Joint Statistical Meetings (JSM), August, 2016, Chicago, Illinois.
Singularity structures and parameter estimation behavior in finite mixtures of distributions. Conference on Statistical Learning and Data Science, June, 2016, University of North Carolina at the Chapel Hill.
Singularity structures and parameter estimation behavior in finite mixtures of distributions. Statistical Machine Learning Student Workshop, June, 2016, University of Michigan, Ann Arbor.
Singularity structures and parameter estimation in mixtures of skew normal distributions. Michigan Student Symposium for Interdisciplinary Statistical
Sciences (MSSISS), March, 2016, Ann Arbor, MI.
Weak identifiability and convergence rate of mixing measures in overfitted Gaussian mixture models. Student Seminar, Department of Statistics, University of Michigan, January, 2016, Ann Arbor, Michigan.
Intrinsic difficulties for the inference of mixing measures in finite mixtures of
univariate skew normal distributions. From Industrial Statistics to Data Science, October, 2015, Ann Arbor, Michigan.
Posterior concentration of mixing parameters in some weakly identifiable finite
mixture models. 10th Conference on Bayesian Nonparametrics, June, 2015, Raleigh,
North Carolina.
Weak identifiability and optimal rate of convergence of mixing measures in overfitted Gaussian mixture models. Statistical Machine Learning Student Workshop, June, 2015, University of
Michigan, Ann Arbor.
Weak identifiability and optimal rate of convergence of mixing measures in
overfitted Gaussian mixture models. NSF Conference  Statistics for Complex
Systems, June, 2015, Madison, Wisconsin.
Optimal convergence rate of parameter estimation in overfitted finite Gaussian
mixture models. Michigan Student Symposium for Interdisciplinary Statistical
Sciences (MSSISS), March, 2015, Ann Arbor, MI.
Identifiability and convergence rate of parameter estimations in exactfitted finite
mixture models. Statistical Machine Learning Student Workshop, June, 2014, University of
Michigan, Ann Arbor.
