Events

Title: Why Random Reshuffling Beats Stochastic Gradient Descent

Abstract:  Over the first few lectures in the seminar we studied the Stochastic Gradient Descent (SGD) method

Title:  A Short Proof of the Containers Theorem for Hypergraphs

Abstract:  A modern trend in extremal combinatorics is extending classical results from the dense setting (e.g. Szemerédi's theorem)

Title: Stability Yields a PTAS for k-Median and k-Means Clustering

Abstract: Previously, we have seen a variety of approximation methods for k-median, and last week we saw

Title: Solving DNN Relaxations of the Quadratic Assignment Problem with ADMM and Facial Reduction

Abstract:

The quadratic assignment problem, QAP, has many important applications ranging from the planning of building locations of a university, to the positioning of modules on a computer chip (VLSI design), to the design of keyboards.

Title: Tutorial on Convolutional Neural Networks

Abstract: We have seen stochastic gradient descent and automatic differentiation to help handle the big dimensionality of problems that often occur in machine learning.

Title: The upper density of monochromatic infinite paths

Abstract: Given a complete graph $K_n$, whose edges are coloured in red and blue, what is the longest monochromatic path one can find?

Title: Implementing supersingular isogeny cryptography

Abstract:

Recent years have seen dramatic progress and improvements in the implementation of supersingular isogeny-based cryptosystems.

Title: Convergence Rate of Block-Coordinate Maximization Burer-Monteiro Method for Solving Large SDP

Abstract: Burer and Monteiro in 2003 proposed a nonlinear algorithm for solving semidefinite programs.

Title: Flag algebras and subgraph density problems

Abstract: Flag algebras are a tool introduced by Razborov in 2007 that has since been successfully used to approach or solve many problems in asymptotic extremal combinatorics.

Title: Clustering under Ordered Norms

Abstract: In this talk we will see approximation algorithms for a broad class of objective functions called as ordered norms.