Statistics Across Campuses

              Adaptive and robust multi-task learning Date :  Thursday, 3 March 2022 Time:   10 am  - 11 am Speaker:  Associate Professor Kaizheng Wang Abstract: In this talk we study the multi-task learning problem that aims to simultaneously analyze multiple datasets collected from different sources and learn one model for each of them. We propose a family of adaptive methods that automatically utilize possible similarities among those tasks while carefully handling their differences. We derive optimal statistical guarantees for the methods and prove their robustness against outlier tasks. Numerical experiments on synthetic and real datasets demonstrate the efficacy of our new methods. The talk is based on joint work with Yaqi Duan. Zoom Link:  Please contact Yanrong Yang ( yanrong.yang@anu.edu.au ) to obtain the zoom link for this seminar.

             Statistical Learning for High-dimensional Tensor Data Date :  Thursday, 17 Februry 2022 Time:   10 am  - 11 am Speaker:  Associate Professor Anru Zhang Abstract: The analysis of tensor data has become an active research topic in statistics and data science recently. Many high order datasets arising from a wide range of modern applications, such as genomics, material science, and neuroimaging analysis, requires modeling with high-dimensional tensors. In addition, tensor methods provide unique perspectives and solutions to many high-dimensional problems where the observations are not necessarily tensors. High-dimensional tensor problems generally possess distinct characteristics that pose unprecedented challenges; there is a clear need to develop novel methods, algorithms, and theory for them. In this talk, we discuss some recent advances in high-dimensional tensor data analysis through several fundamental topics and their applications in microscopy imaging and neuroimaging.

            Prediction using many samples with models possibly containing partially shared parameters Date :  Thursday, 10 Februry 2022 Time:   10 am  - 11 am Speaker:  Professor Xinyu Zhang  ( University of Chinese Academy Sciences ) Abstract: We consider prediction based on a main model. When the main model shares partial parameters with several other helper models, we make use of the additional information. Specifically, we propose a model averaging prediction (MAP) procedure that takes into account data related to the main model as well as data related to the helper models. We allow the data related to different models to follow different structures, as long as they share some common covariate effect. We show that when the main model is mis-specified, MAP yields the optimal weights in terms of prediction. Further, if the main model is correctly specified, then MAP will automatically exclude all incorrect helper models asymptotically. Simulation studies are conducted to demonstrate

    Methods for scalable probabilistic inference Date: Tuesday, 8 February 2022   Time: 16 :00 (UTC)     Speaker:  Dan Foreman-Mackey (Flatiron Institute, USA) Contact the organizer: Andriy Olenko a.olenko@latrobe.edu.au Abstract:  Most data analysis pipelines in astrophysics now have some steps that require detailed probabilistic modeling. As datasets get larger and our research questions get more ambitious, we are often pushing the limits of what our statistical frameworks are capable of. In this talk, I will discuss recent (and not so recent) developments in the field probabilistic programming that enable rigorous Bayesian inference with large datasets, and high-dimensional or computationally expensive models. In particular, I will highlight some scalable methods for time series analysis using Gaussian Processes, and some of the open source tools and computational techniques that have the potential to be broadly useful for accelerating inference in astrophysics.     See zo