Theoretical Machine Learning Seminar

Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowd-sourced and contain sensitive information...

Linear dynamical systems (LDSs) are a class of time-series models widely used in robotics, finance, engineering, and meteorology. I will present our "spectral filtering" approach to the identification and control of discrete-time LDSs with multi...

The current successes of deep neural networks have largely come on classification problems, based on datasets containing hundreds of examples from each category. Humans can easily learn new words or classes of visual objects from very few examples...

A fundamental problem in Bayesian statistics is sampling from distributions that are only specified up to a partition function (constant of proportionality). In particular, we consider the problem of sampling from a distribution given access to the...

With the continuing successes of deep learning, it becomes increasingly important to better understand the phenomena exhibited by these models, ideally through a combination of systematic experiments and theory. In this talk I discuss some of our...

Our goal is to create a convenient natural language interface for performing well-specified but complex actions such as analyzing data, manipulating text, and querying databases. However, existing natural language interfaces for such tasks are quite...

I will describe recent efforts in using machine learning to control cooling in Google data centers, as well as related research in linear quadratic control.

Modern machine learning algorithms often involve complicated models with tunable parameters, called hyperparameters, that are set during training. Hyperparameter tuning/optimization is considered an art. (See e.g. http://www.argmin.net/2016/06/20...