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.
Mathematics students learn a powerful technique for proving theorems about an arbitrary natural number: the principle of mathematical induction. This talk introduces a closely related proof technique called "path induction," which can be thought of as an expression of Leibniz's "indiscernibility of identicals": if x and y are identified, then th...
Creator:
Riehl, Emily (Johns Hopkins University)
Created:
2022-08-03
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Cohomological ideas have recently been injected into persistent homology and have for example been used for accelerating the calculation of persistence diagrams by softwares, such as Ripser. The cup product operation which is available at cohomology level gives rise to a graded ring structure that extends the usual vector space structure and is ...
Creator:
Zhou, Ling (The Ohio State University)
Created:
2022-08-02
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Persistent homology is a main tool in topological data analysis. So it is natural to ask how strong this quantifier is and how much information is lost. There are many ways to ask this question. Here we will concentrate on the case of level set filtrations on simplicial sets. Already the example of a triangle yields a rich structure with the Möb...
Creator:
Tillmann, Ulrike (University of Oxford)
Created:
2022-08-03
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
I will review the theory of ramification in number theory and then show that being totally ramified or unramified is equivalent to a natural condition in higher algebra. This leads to a much simplified calculation of THH of a ring of integers in a number field, relying on ramified descent (a kind of weaker etale descent).
Creator:
Berman, John (University of Massachusetts)
Created:
2022-08-03
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.
Sarah Reyes was born in the Phlippines and moved to the U.S. when she was in middle school. Though she faced a lot of racial discrimination in her first years of being here, she eventually found a Filipino American community that she is proud to be a part of now.
Creator:
Haakonsen, Malia
Contributor:
Rolland, Isabella
Created:
2022-05-02
Contributed By:
University of Minnesota, Immigration History Research Center
Federated machine learning (FL) is gaining a lot of traction across research communities and industries. FL allows machine learning (ML) model training without sharing data across different parties, thus natively supporting data privacy. However, designing and executing FL jobs is not an easy task today. Flame is an open-source project that aims...
Creator:
Le, Myungjin (Cisco)
Created:
2022-04-29
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
I would like to talk about the interaction of traditional algebraic topology and homotopy theory with applied topology, and specifically describe specifically some opportunities for better integration of "higher tech" techniques into applications.
Creator:
Carlsson, Gunnar (Stanford University)
Created:
2022-08-05
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In recent years, the need to accommodate non-Euclidean structures in data science has brought a boom in deep learning methods on graphs, leading to many practical applications with commercial impact. In this talk, we will review the mathematical foundations of the generalization capabilities of graph convolutional neural networks (GNNs). We will...
Creator:
Levie, Ron (Ludwig-Maximilians-Universität München)
Created:
2022-02-01
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.