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Title: Rapid mixing of Glauber dynamics for colorings below Vigoda’s 11/6 threshold 

Abstract:

A well-known conjecture in computer science and statistical physics is that Glauber dynamics on the set of k-colorings of a graph GÌý´Ç²Ô n vertices with maximum degree Δ is rapidly mixing for k ≥ Δ+2.

Title: Naji’s characterization of circle graphs

Abstract: A circle graph is the intersection graph of chords of a circle.

Title: A characterization of (p,q)-mixing when p/q < 4

Abstract: Let Hom(G,H) be the graph whose vertex set is the set of H-colourings of G, and two H-colourings f and g are adjacent if f differs from g in at most one vertex.

Title: The Shapiro-Shapiro Conjecture

Abstract:

Given four lines in 3-space, can you find a fifth line that intersects the other four? How many?

This is the smallest non-trivial example of a "Schubert problem". The answer, in this case, is not hard to compute: there are two such lines. Generalizations of this fact date back to 19th century work of Schubert.

Title: Review of martingale theory, stochastic gradient descent, and adaptive line-line-search for stochastic optimization.

Abstract: With the rise of large data sets, practical algorithms for machine learning often use probability and statistics.

Title: Controllable graphs

Abstract: A graph is controllable if no eigenvector is orthogonal to the all-ones vector.

Title: Representable orientable matroids that are not real-representable

Abstract: In this talk we will have a brief introduction to oriented matroids and their relation to real-representability.

Title: Offline Assisted Group Key Exchange

Abstract:

This talk will focus on the problem of forward secrecy in group key exchange (GKE), where most of the participants remain offline until they wish to compute the key.

Title: Basis Shape Loci and the Positive Grassmannian

Abstract: We study the set of k-dimensional planes in R^n admitting a basis of vectors with prescribed supports.

Title: Tutorial on back-propagation and automatic differentiation

Abstract: In this presentation, I'll cover the basics of automatic differentiation (AD).