Theoretical Machine Learning Seminar

Recent progress in optimization theory has shown how we may harness second-order, i.e. Hessian, information, for achieving faster rates for both convex and non-convex optimization problems. Given this observation, it is then natural to ask what sort...

Recent empirical works have successfully used unlabeled data to learn feature representations that are broadly useful in downstream classification tasks. Several of these methods are reminiscent of the well-known word2vec embedding algorithm...

The (stochastic) gradient descent and the multiplicative update method are probably the most popular algorithms in machine learning. We introduce and study a new regularization which provides a unification of the additive and multiplicative updates...

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...

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...