In this talk, Hande will give an overview of some of the best practices a data scientist should know. These will include topics like virtual environments, utilizing functions, code documentation and other things that you could start incorporating in your data science projects or coding in general. She will also include some quick tips and advice...
Creator:
Tuzel, Hande (Sabre Corporation)
Created:
2022-02-11
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Value creation in private equity investment portfolios is fundamental to delivering results for PE customers. Our focus is in the energy and transportation sectors, and by having deep understanding of how these industries work, we explore applications where advanced analytics and better use of data can create more efficient operations and growth...
Creator:
Einset, Erik (Global Infrastructures Partners)
Created:
2022-04-01
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
This talk will describe a suite of physically inspired instruments we've developed to enable exploration of large-scale text data, illuminate collective behavioral patterns, and develop a science of stories. Along with our flagship efforts at http://hedonometer.org and https://storywrangling.org we show how Instagram photos reveal markers of dep...
Creator:
Kileel, Joe (The University of Texas at Austin)
Created:
2022-02-08
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Clustering algorithms based on mean shift or spectral methods on graphs are ubiquitous in data analysis. However, in practice, these two types of algorithms are treated as conceptually disjoint: mean shift clusters based on the density of a dataset, while spectral methods allow for clustering based on geometry. In joint work with Nicolás ...
Creator:
Craig, Katy (University of California, Santa Barbara)
Created:
2022-02-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Modern learning algorithms such as deep neural networks operate in regimes that defy the traditional statistical learning theory. Neural networks architectures often contain more parameters than training samples. Despite their huge complexity, the generalization error achieved on real data is small. In this talk, we aim to study the generalizati...
Creator:
Seroussi, Inbar (Weizmann Institute of Science)
Created:
2022-04-12
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Many diseases are complex heterogeneous conditions that affect multiple organs in the body and depend on the interplay between several factors that include molecular and environmental factors, thus requiring a holistic approach in understanding the complexity and heterogeneity. In this talk, I will present some of our current statistical and ma...
Creator:
Safo, Sandra (University of Minnesota, Twin Cities)
Created:
2022-02-22
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In the context of numerical linear algebra algorithms, where it is natural to sacrifice accuracy in return for quicker computation of solutions whose errors are only slightly larger than optimal, the time-accuracy tradeoff of randomized sketching has been well-characterized. Algorithms such as Blendenpik and LSRN have shown that carefully design...
Creator:
Gittens, Alex (Rensselaer Polytechnic Institute)
Created:
2022-01-25
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In this talk, I focus on the multivariate method of moments for parameter estimation. First from a theoretical standpoint, we show that in problems where the noise is high, the number of observations necessary to estimate parameters is dictated by the moments of the distribution. Second from a computational standpoint, we address the curse of di...
Creator:
Pereira, Joao (The University of Texas at Austin)
Created:
2022-04-05
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Adversarial training is a framework widely used by machine learning practitioners to enforce robustness of learning models. Despite the development of several computational strategies for adversarial training and some theoretical development in the broader distributionally robust optimization literature, there are still several theoretical quest...
Creator:
Garcia Trillos, Nicolas (University of Wisconsin, Madison)
Created:
2022-03-01
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
The assumption that high dimensional data is Gaussian is pervasive in many statistical procedures, due not only to its tail decay, but also to the level of analytic tractability this special distribution provides. We explore the relaxation of the Gaussian assumption in Single Index models and Shrinkage estimation using two tools that originate i...
Creator:
Goldstein, Larry (University of Southern California)
Created:
2022-03-29
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
