Word Embeddings: Explaining their properties

This is a followup to an earlier post about word embeddings, which capture the meaning of a word using a low-dimensional vector, and are ubiquitous in natural language processing. I will talk about my joint... Continue

NIPS 2015 workshop on non-convex optimization

While convex analysis has received much attention by the machine learning community, theoretical analysis of non-convex optimization is still nascent. This blog as well as the recent NIPS 2015 workshop on non-convex optimization aim to... Continue

Nature, Dynamical Systems and Optimization

The language of dynamical systems is the preferred choice of scientists to model a wide variety of phenomena in nature. The reason is that, often, it is easy to locally observe or understand what happens... Continue

Tensor Methods in Machine Learning

Tensors are high dimensional generalizations of matrices. In recent years tensor decompositions were used to design learning algorithms for estimating parameters of latent variable models like Hidden Markov Model, Mixture of Gaussians and Latent Dirichlet... Continue

Semantic Word Embeddings

This post can be seen as an introduction to how nonconvex problems arise naturally in practice, and also the relative ease with which they are often solved. I will talk about word embeddings, a geometric... Continue

Why go off the convex path?

The notion of convexity underlies a lot of beautiful mathematics. When combined with computation, it gives rise to the area of convex optimization that has had a huge impact on understanding and improving the world... Continue