Video: https://vimeo.com/185052120 I will talk about three simple combinatorial ideas to speed up parallel and distributed learning algorithms. We will start off with serial equivalence in asynchronous parallel ML, its significance, and how we can guarantee it using a recent phase transition result on graphs. We continue on the issue …
Video: https://vimeo.com/184352560 Concepts in geometry often have parallels in information theory; for example, volume and entropy, surface area and Fisher information, sphere-packing and channel coding, and Euclidean balls and Gaussian distributions, to name a few. These similarities provide a simple way to posit theorems in one area by translating the …
Video: https://vimeo.com/183315365 Projected gradient descent (PGD), and its close variants, are often considered the method of choice for solving a very large variety of machine learning optimization problems, including sparse recovery problems, empirical risk minimization, stochastic optimization, and online convex optimization. This is not surprising, since PGD is often optimal …
Video: https://vimeo.com/154897631 We use CVaR to create a general framework for computing compromise solutions in a multi-objective, multi-stakeholder setting. In this setting, we sample the preferences of a population of stakeholders and we observe that the stakeholder dissatisfactions (distance to their utopia points) can be interpreted as random variables. Consequently, …
Video : https://vimeo.com/182451507 We propose the combinatorial inference to explore the global topological structures of graphical models.In particular, we conduct hypothesis tests on many combinatorial graph properties including connectivity, hub detection, perfect matching, etc. Our methods can be applied to any graph property which is invariant under the deletion of …