SMI

Building Map

Testing One Hypothesis Multiple Times: a simple tool for generalized inference

Time
Speaker
Sara Algeri

The identification of new rare signals in data, the detection of a sudden change in a trend, and the selection of competing models, are some among the most challenging problems in statistical practice.

Building
Room
211

Manifold Data Analysis with Applications to High-Resolution 3D Imaging

Time
Speaker
Matthew Reimherr

Many scientific areas are faced with the challenge of extracting information from large, complex, and highly structured data sets. A great deal of modern statistical work focuses on developing tools for handling such data. In this work we presents a new subfield of functional data analysis, FDA, which we call Manifold Data Analysis, or MDA. MDA is concerned with the statistical analysis of samples where one or more variables measured on each unit is a manifold, thus resulting in as many manifolds as we have units.

Building
Room
211

Constructing Stabilized Dynamic Treatment Regimes Using Observational Data

Time
Speaker
Yingqi Zhao

Abstract:

Dynamic treatment regimes (DTRs) are sequential decision regimes for individual patients that can adapt over time to an evolving illness. The goal is to find the DTRs tailored to individual characteristics that lead to the best long term outcome if implemented. In many clinical applications, it is desirable to provide a fixed decision rule over time for the patients.

Building
Room
211

On Combining Point and Area Measurements for Annual Runoff Predictions - A Non-stationary Spatial Model

Time
Speaker
Jingyi Guo

Abstract:

This work is motivated by the problem of ungauged basins, and the aim is to make inference about basins based on both point observations from precipitation gauges and areal measurements from other basins in the same area. As precipitation (and evaporation) are non-stationary spatial processes due to topology, we set up a spatial non-stationary model with elevation as an explanatory variable in the dependency structure.

Building
Room
211

Composite Modeling and Optimization, with Applications to Phase Retrieval and Nonlinear Observation Modeling

Time
Speaker
John Duchi

Abstract:

We consider minimization of stochastic functionals that are compositions of a (potentially) non-smooth convex function h and smooth function c. We develop two stochastic methods--a stochastic prox-linear algorithm and a stochastic (generalized) sub-gradient procedure--and prove that, under mild technical conditions, each converges to first-order stationary points of the stochastic objective.

Building
Room
211

Confidence Sets for Phylogenetic Trees

Time
Speaker
Amy Willis
 

Abstract:

Phylogenetic trees represent evolutionary histories and have many important applications in biology, anthropology and criminology. The branching structure of the tree encodes the order of evolutionary divergence, and the branch lengths denote the time between divergence events.

Building
Room
211

From safe screening rules to working sets for faster Lasso-type solvers

Time
Speaker
Joseph Salmon

Convex sparsity promoting regularizations are now ubiquitous to regularize inverse problems in statistics, in signal processing and in machine learning. By construction, they yield solutions with few non-zero coefficients. This point is particularly appealing for Working Set (WS) strategies, an optimization technique that solves simpler problems by handling small subsets of variables, whose indices form the WS. Such methods involve two nested iterations: the outer loop corresponds to the definition of the WS and the inner loop calls a solver for the subproblems.

Building
Room
211

Yule's "Nonsense Correlation" Solved!

Time
Speaker
Philip Ernst

In this talk, I will discuss how I recently resolved a longstanding open statistical problem. The problem, formulated by the British statistician Udny Yule in 1926, is to mathematically prove Yule's 1926 empirical finding of ``nonsense correlation.” We solve the problem by analytically determining the second moment of the empirical correlation coefficient of two independent Wiener processes. Using tools from Fredholm integral equation theory, we calculate the second moment of the empirical correlation to obtain a value for the standard deviation of the empirical correlation of nearly .5.

Building
Room
211

Graph Structured Signal Processing

Time
Speaker
James Sharpnack

Signal processing on graphs is a framework for non-parametric function estimation and hypothesis testing that generalizes spatial signal processing to heterogeneous domains. I will discuss the history of this line of research, highlighting common themes and major advances. I will introduce various graph wavelet algorithms, and highlight any known approximation theoretic guarantees. Recently, it has been determined that the fused lasso is theoretically competitive with wavelet thresholding under some conditions, meaning that the fused lasso is also a locally adaptive smoothing procedure.

Building
Room
211

A SMART Stochastic Algorithm for Nonconvex Optimization

Time
Speaker
Aleksandr Y. Aravkin

We show how to transform any optimization problem that arises from fitting a machine learning model into one that (1) detects and removes contaminated data from the training set and (2) simultaneously fits the trimmed model on the remaining uncontaminated data. To solve the resulting nonconvex optimization problem, we introduce a fast stochastic proximal-gradient algorithm that incorporates prior knowledge through nonsmooth regularization.

Building
Room
211