Carson Mosso - Latent Association Mining in Binary Data We will introduce a new data mining method for binary valued data, called Latent Association Mining in Binary Data. The origin of this problem is in market basket analysis, where … Read more
Mark He UNC-Chapel Hill Intertemporal Community Detection in Human Mobility Networks: Case Studies of Bikeshare Systems in US Cities We introduce a community detection method that finds clusters in network time-series by introducing a method that finds significantly … Read more
Graduate Seminar Friday, March 6th, 2020 120 Hanes Hall 3:30pm Alex Touzov UNC-Chapel Hill Weak infeasibility in semidefinite programming: a complete characterization and generating all instances Abstract: In this work, we analyze weakly infeasible semidefinite programs (SDPs). … Read more
Optimal Transport for Stationary Markov Chains via Policy Iteration In this talk, we discuss an extension of optimal transport techniques to stationary Markov chains from a computational perspective. In this context, we show that the standard optimal transport problem does … Read more
Dr. Shankar Bhamidi, Director of Graduate Studies, will host a zoom chat with grad students. Come with questions, or just check in and enjoy social time.
Quoc Tran-Dinh Statistics & Operations Research Dept., UNC-CH Efficient Stochastic Gradient-Based Algorithms with Biased Variance-Reduced Estimators In this talk, we discuss some recent progress in stochastic gradient-based methods using biased variance-reduced estimators to approximate a stationary point or a KKT … Read more
Stochastic Approximation of Quasi-Stationary Distributions We propose two numerical schemes for approximating quasi-stationary distributions (QSD) of finite state Markov chains with absorbing states. Both schemes are described in terms of certain interacting chains in which the interaction is given in … Read more
Michael Conroy UNC-Chapel Hill Efficient rare-event simulation for branching processes In this talk I’ll discuss some of my past, current, and future work with importance sampling schemes for maxima of branching processes. In a recent paper, my collaborators and I … Read more
Samopriya Basu Fiducial inference for SDEs with constant diffusion In this talk, I will talk about my research with my advisor Prof. Jan Hannig on carrying out fiducial inference for stochastic ordinary differential equations with constant diffusion coëfficient. The diffusion … Read more
Stefanos Kechagias Analytical Consulting & Enterprise Solutions, SAS Institute Scratch out “learn TensorFlow” from your New Year bucket list. In goes “learn how to tell valuable stories” In this talk we will discuss three Story Telling Mediums statisticians employ to … Read more
Generalized Fiducial Inference on Differentiable Manifolds I’ll discuss the problem of defining a general fiducial density on an implicitly defined differentiable manifold and introduce our recent solution. Our proposed density extends the usual generalized fiducial distribution (GFD) by projecting the … Read more
Kentaro Hoffman What’s In a Resolution: A Dempster-Schafer approach to Multinomial Hypothesis Tests Resolution is an omnipresent part of statistical analysis. It comes up in introductory statistics when we are asked if we should treat time as a continuous variable … Read more
