Sums of Squares and Perfect Graphs The problem of optimizing over the set of nonnegative polynomials naturally arises in a variety of applications in different areas. These include control theory, statistics, game theory, and combinatorial optimization. While this problem is … Read more
Semidefinite programs (SDPs) are some of the most popular and widespread optimization problems to emerge in the last thirty years. A curious pathology of SDPs is illustrated by a famous example of Khachiyan: feasible solutions of SDPs may need exponential space to … Read more
Julie Swann Department of Industrial and Systems Engineering North Carolina State University Analytics to Promote an Efficient, Effective, and Equitable Response to the Covid-19 Pandemic Planning and response to the Covid-19 pandemic is complex but can be informed by analytics … Read more
Convergence of Gaussian kernelized graph Laplacian: eigen-convergence and bi-stochastic normalization Consider kernelized graph affinity matrix constructed from $N$ data points i.i.d. sampled from a general unknown $d$-dimensional manifold embedded in a possibly high-dimensional space. The setting is generic in graph-based … Read more
Reinforcement Learning under Unmeasured Confounding In practical reinforcement learning (RL), a representation of the full state which makes the system Markovian and therefore amenable to most existing RL algorithms is not known a priori. Decision makers are often facing so-called … Read more
Turning the data-integration dial: efficient inference from different data sources A fundamental aspect of statistics is the integration of data from different sources. Classically, Fisher and others were focused on how to integrate homogeneous sets of data. More recently, the … Read more
Modern Nonconvex Optimization: Parametric and Stochastic Programming Extensions Many modern applications in data science and operations research can be casted as nonconvex and nonsmooth optimization problems, which usually cannot be rigorously handled by applying heuristics or imposing some naive simplications … Read more
Principal Component adjustments for High Dimensional Association Studies In this talk, I will consider the effect of Principal Component (PC) adjustments while inferring the effects of variables on outcomes. This problem is particularly motivated by applications in genetic association studies … Read more
Survey Descent: A Multipoint Generalization of Gradient Descent for Nonsmooth Optimization First-order minimization algorithms, which rely only on blackbox (sub)gradient oracles, have always played a central role in the field of optimization, and are now even more prominent due to … Read more
From HeartSteps to HeartBeats: Personalized Decision-making Ever-increasing access to data and computational power allows us to make decisions that are personalized to users by taking their behaviors and contexts into account. These developments are especially useful in domains like mobile … Read more
On the Computational Complexity of Semidefinite and Polynomial Optimization: A real Algebraic Geometry Approach Semidefinite and polynomial optimization (SDO and PO) are topics of great theoretical and practical interest, with numerous applications in theoretical computer science, control theory, and statistics. … Read more
Statistical Thresholds for Tensor PCA Recent work in theoretical physics has produced numerous conjectures linking problems in high- dimensional statistics to the behavior of disordered magnetic materials known as spin glasses. In this talk, I will discuss results from joint … Read more
