Seminars

I will talk about a recent result showing that some well-studied polynomial-based error-correcting codes (Folded Reed-Solomon Codes and Multiplicity Codes) are "list-decodable upto capacity with constant list-size".

At its core, this is a statement...

A promising class of generative models maps points from a simple distribution to a complex distribution through an invertible neural network. Likelihood-based training of these models requires restricting their architectures to allow cheap...

The emerging theory of High-Dimensional Expansion suggests a number of inherently different notions to quantify expansion of simplicial complexes. We will talk about the notion of local spectral expansion, that plays a key role in recent advances in...

I will give a gentle overview of my work with Petter Brändén on Lorentzian polynomials (

https://arxiv.org/abs/1902.03719

), which link continuous convex analysis and discrete convex analysis via tropical geometry. The class contains homogeneous...

A matroid is an abstract combinatorial object which generalizes the notions of spanning trees, and linearly independent sets of vectors. I will talk about an efficient algorithm based on the Markov Chain Monte Carlo technique to approximately count...

Lorentzian polynomials link continuous convex analysis and discrete convex analysis via tropical geometry. The class of Lorentzian polynomials contains homogeneous stable polynomials as well as volume polynomials of convex bodies and projective...

Suppose an agent is in an unknown Markov environment in the absence of a reward signal, what might we hope that an agent can efficiently learn to do? One natural, intrinsically defined, objective problem is for the agent to learn a policy which...

The current era of large scale machine learning powered by Deep Learning methods has brought about tremendous advances, driven by the lightweight Stochastic Gradient Descent (SGD) method. Despite relying on a simple algorithmic primitive, this era...