Algebraic and Geometric Methods in Statistics

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Home Questions Tags Users Unanswered. Applications of algebraic geometry to machine learning Ask Question. Asked 3 years, 6 months ago. Active 9 months ago.

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Mannucci Mar 19 '16 at Paul Siegel Paul Siegel Persistence cohomology is quite a thing at the first glance in cosmology as well as neural data due to its scaling-resistant. But the more I delved into it I agree with your comment more that it just formally involve in the data. L Mar 14 '17 at Maybe I am wrong, but what I think AG should provide is a theoretical framework underlying the data Zachary Hamaker 9 9 silver badges 24 24 bronze badges. Piotr Zwiernik Piotr Zwiernik 2 2 bronze badges.

Indeed, to some extent deep learning is necessary because gradient descent tends to systematically fail in fully connected multi-layered nets. I think it would be awesome if somehow deep mathematics that was previously considered 'pure' turned up in a solution. I certainly don't know of any such solutions at the moment. There are a lot of very interesting mathematical questions associated with nets. How about- "probabilistically, why do neural nets work? Or - " how does the choice of the error function effect the probabilities of the tacit distribution function". I can think of others.

Much like my comment about DAG's , one can fantasize.

Meanwhile, in the real world, I got yelled at for calling Google's TensorFlow, a numerical linear algebra library. Not that there's anything wrong with numerical linear algebra :. This seminar features talks on interdisciplinary research which involves applications of fields related to algebra, geometry, topology, and combinatorics to fields like statistics, optimization, computer science, electrical engineering, biology, physics, and other sciences. One of the main goals of the seminar is to connect people from pure mathematics with people from applied fields.

Mailing list: If you would like to be added to the mailing list, please contact one of the organizers above. In the spring of , the seminar will resume meeting every other week. It now has a new new name: "Seminar on Algebra, Statistcs, and Optimization". To make data-driven decisions in large-scale systems, learning the statistical associations of different elements is not sufficient.

Algorithms need to be equipped with the ability to learn the underlying causal mechanism.

Meanwhile, intelligent decision making also requires us to effectively incorporate domain knowledge from various application domains to improve the statistical efficiency as well as the robustness of existing statistical methods. Probabilistically, such domain knowledge can be incorporated as structural constraints on the underlying distribution. Exploiting these underlying structures calls for new statistical methods for inference with various distributional constraints. In this talk, I will present my recent progresses for addressing the two fundamental statistical challenges stated above, namely 1 establishing the underlying causal mechanism in complex systems, and 2 incorporating structural constraints to statistical inference.

Algebraic statistics - Wikipedia

In the fall of , we are going to hold an Applied Algebra Day. Here are the logistics. Applied Algebra Day. In this talk I will discuss our recent work describing the random behavior of monomial ideals.

Applications of Algebraic Geometry

Our work is a natural generalization of classical work on random graphs and random simplicial complexes. Under this model we outlined some basic properties of random monomial ideals. For example, I will present theorems about the probability distributions, expectations and thresholds for events of monomial ideals with given Krull dimension, Hilbert function, and the average behavior of minimal-free resolutions.

We consider the non-symmetric rank and the symmetric rank of cubic surfaces, and show that the two notions coincide over the complex numbers. It will present a focused activity parallel to the program on Algebraic Methods in Systems Biology and Statistics being hosted by the Statistical and Applied Mathematical Sciences Institute. See below for the workshop schedule.

Accomodations: A block of rooms has been reserved at the Rose Garden Inn. Reservations may be made by calling OR directly on their website. Click on Corporate at the bottom of the screen and when prompted enter code MATH this code is not case sensitive. By using this code a new calendar will appear and will show MSRI rate on all room types available. The cut-off date for reservations is December 1, A block of rooms has been reserved at the Hotel Durant. Please mention the workshop name and reference the following code when making reservations via phone, fax or e-mail: MSRIAL.

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