2020 Fall

Date: 2020-12-04 Time: 15:30-16:30 Zoom Link Meeting ID: 924 5390 4989 Passcode: 690084 Abstract: A novel approach based on generative neural networks is introduced for constructing quasi-random number generators for multivariate models with any underlying copula in order to estimate expectations with variance reduction. So far, quasi-random number generators for multivariate distributions required a careful design, exploiting specific properties (such as conditional distributions) of the implied copula or the underlying quasi-Monte Carlo point set, and were only tractable for a small number of models.

Date: 2020-11-27 Time: 15:30-16:30 Zoom Link Meeting ID: 924 5390 4989 Passcode: 690084 Abstract: In contemporary scientific research, it is often of great interest to predict a categorical response based on a high-dimensional tensor (i.e. multi-dimensional array). Motivated by applications in science and engineering, we propose two probabilistic methods for machine learning on tensor data in the supervised and the unsupervised context, respectively. For supervised problems, we develop a comprehensive discriminant analysis model, called the CATCH model.

Date: 2020-11-20 Time: 15:30-16:30 Zoom Link Meeting ID: 924 5390 4989 Passcode: 690084 Abstract: Despite the success of combined antiretroviral therapy (ART) in achieving sustained control of viral replication, the concerns about side-effects, drug-drug interactions, drug resistance and cost call for a need to identify strategies for achieving HIV eradication or an ART-free remission. Following ART withdrawal, patients’ viral load levels usually increase rapidly to a peak followed by a dip, and then stabilize at a viral load set point.

Date: 2020-11-13 Time: 15:30-16:30 Zoom Link Abstract: The error or variability of statistical and machine learning algorithms is often assessed by repeatedly re-fitting a model with different weighted versions of the observed data. The ubiquitous tools of cross-validation (CV) and the bootstrap are examples of this technique. These methods are powerful in large part due to their model agnosticism but can be slow to run on modern, large data sets due to the need to repeatedly re-fit the model.

Date: 2020-11-06 Time: 15:30-16:30 Zoom Link Meeting ID: 924 5390 4989 Passcode: 690084 Abstract: I will introduce Generalized Energy Based Models (GEBM) for generative modelling. These models combine two trained components: a base distribution (generally an implicit model), which can learn the support of data with low intrinsic dimension in a high dimensional space; and an energy function, to refine the probability mass on the learned support. Both the energy function and base jointly constitute the final model, unlike GANs, which retain only the base distribution (the “generator”).

Date: 2020-10-30 Time: 15:30-16:30 Zoom Link Meeting ID: 924 5390 4989 Passcode: 690084 Abstract: Parallel randomized clinical trial (RCT) and real-world data (RWD) are becoming increasingly available for treatment evaluation. Given the complementary features of the RCT and RWD, we propose a test-based integrative analysis of the RCT and RWD for accurate and robust estimation of the heterogeneity of treatment effect (HTE), which lies at the heart of precision medicine. When the RWD are not subject to bias, e.

Date: 2020-10-23 Time: 15:30-16:30 Zoom Link Meeting ID: 924 5390 4989 Passcode: 690084 Abstract: Linear regression on a set of observations linked by a network has been an essential tool in modeling the relationship between response and covariates with additional network data. Despite its wide range of applications in many areas, such as social sciences and health-related research, the problem has not been well-studied in statistics so far. Previous methods either lack of inference tools or rely on restrictive assumptions on social effects, and usually treat the network structure as precisely observed, which is too good to be true in many problems.

Date: 2020-10-16 Time: 15:30-16:30 Zoom Link Meeting ID: 924 5390 4989 Passcode: 690084 Abstract: Markov chain Monte Carlo (MCMC) algorithms, such as the Metropolis Algorithm and the Gibbs Sampler, are an extremely useful and popular method of approximately sampling from complicated probability distributions. Adaptive MCMC attempts to automatically modify the algorithm while it runs, to improve its performance on the fly. However, such adaptation often destroys the ergodicity properties necessary for the algorithm to be valid.

Date: 2020-10-09 Time: 15:30-16:30 Zoom Link Meeting ID: 924 5390 4989 Passcode: 690084 Abstract: Statistical learning theory is by now a mature branch of data science that hosts a vast variety of practical techniques for tackling data-related problems. In this talk we present some fundamental concepts upon which statistical learning theory has been based. Different approaches to statistical inference will be discussed and the main problem of learning from Vapnik’s point of view will be explained.

Date: 2020-10-02 Time: 15:30-16:30 Zoom Link Meeting ID: 939 8331 3215 Passcode: 096952 Abstract: Data science is discussed along with some historical perspective. Selected problems in classification are considered, either via specific datasets or general problem types. In each case, the problem is introduced before one or more potential solutions are discussed and applied. The problems discussed include data with outliers, longitudinal data, and three-way data. The proposed approaches are generally mixture model-based.