Abstract:  Sequential Multiple Assignment Randomized Trials (SMARTs) are considered the gold standard for estimation and evaluation of treatment regimes. SMARTs are typically sized to ensure sufficient power for a simple comparison, e.g., the comparison of two fixed treatment sequences. Estimation of an optimal treatment regime is conducted as part of a secondary and hypothesis-generating analysis with formal evaluation of the estimated optimal regime deferred to a follow-up trial. However, running a follow-up trial to evaluate an estimated optimal treatment regime…

Abstract: Prioritizing likely causal genes from genome-wide association studies (GWAS) is a fundamental problem. There are many methods for GWAS gene prioritization, including methods that map candidate SNPs to their target genes and methods that leverage patterns of enrichment from across the genome. In this talk, I will introduce a new method for leveraging genome-wide patterns of enrichment to prioritize genes at GWAS loci, incorporating information about genes from many sources. I will then discuss the problem of benchmarking gene prioritization methods,…

Abstract: Many areas of the physical, engineering and biological sciences make extensive use of computer simulators to model complex systems. Confidence sets and hypothesis testing are the hallmarks of statistical inference, but classical methods are poorly suited for scientific applications involving complex simulators without a tractable likelihood. Recently, many techniques have been introduced that learn a surrogate likelihood using forward-simulated data, but these methods do not guarantee frequentist confidence sets and tests with nominal coverage and Type I error control,…

Title: Interpolation and learning with scale dependent kernels Abstract:  We study the learning properties of nonparametric ridge-less least squares. In particular, we consider the common case of estimators defined by scale dependent (Matern) kernels, and focus on the role scale and smoothness. These estimators interpolate the data and the scale can be shown to control their stability to noise and sampling.  Larger scales, corresponding to smoother functions, improve stability with respect to sampling. However, smaller scales, corresponding to more complex functions,…

Abstract:  Self-supervised learning is an increasingly popular approach for learning representations of data that can be used for downstream representation tasks. A practical advantage of self-supervised learning is that it can be used on unlabeled data. However, even when labels are available, self-supervised learning can be competitive with the more "traditional" approach of supervised learning. In this talk we consider "self supervised + simple classifier (SSS)" algorithms, which are obtained by first learning a self-supervised classifier on data, and then…

Abstract: Matrix completion is the study of recovering an underlying matrix from a sparse subset of noisy observations. Traditionally, it is assumed that the entries of the matrix are “missing completely atrandom” (MCAR), i.e., each entry is revealed at random, independent of everything else, with uniform probability. This is likely unrealistic due to the presence of “latent confounders”, i.e., unobserved factors that determine both the entries of the underlying matrix and the missingness pattern in the observed matrix.  In general,…

Abstract: Motivated by applications such as particle tracking, network de-anonymization, and computer vision, a recent thread of research is devoted to statistical models of assignment problems, in which the data are random weight graphs correlated with the latent permutation. In contrast to problems such as planted clique or stochastic block model, the major difference here is the lack of low-rank structures, which brings forth new challenges in both statistical analysis and algorithm design. In the first half of the talk,…