Jianing Shi : April 26th

I will discuss Nesterov's optimal gradient method at the group meeting.  I will talk about Nesterov's method for minimizing composite objective function, together with its implication for L1 minimization. 

There is unfortunately no short story on Nesterov's method, however you can find his work at

http://www.core.ucl.ac.be/~nesterov/

Jianing's nicely done slides can be found here.

Jonathan Huggins : April 19

Submodularity part II, starting at 5:45pm.

After a brief review of two weeks ago, I will describe Queyranne's efficient and fully combinatorial algorithm for minimizing symmetric submodular functions. Next, I will give the details of the convex Lovasz extension of submodular functions, including a sketch of the proof of how to efficiently calculate the extension. Finally, I'll discuss portions of a recent paper on decomposable submodular functions by Stobbe and Krause, emphasizing its application to Markov Random Fields and the connections to the Lovasz extension and concave functions

Tim Machado : April 12

Learning Dictionaries of Stable Autoregressive Models for Audio Scene Analysis by Youngmin Cho and Lawrence K. Saul

Jonathan Huggins: April 5

This week I will be talking about submodular set functions, which possess a useful and intuitive diminishing returns property. I will begin with the definition and give a variety of examples of situations in which submodular functions arise. I'll discuss some connections to convex and concave functions, as well as strategies for minimization and maximization. I will mainly draw from the classic paper "Submodular functions and convexity" by Lovász, as well as a recent paper by Stobbe and Krause, which provides a nice summary of key results, as well as a discussion of many of the advances since the Lovász paper. If the talk sparks your interest, then I highly recommend the tutorial (complete with hours of video!) by Krause and Guestrin.