One empirical finding in deep learning is that simple methods such as stochastic gradient descent (SGD) have a remarkable ability to fit training data. From a capacity perspective, this may not be surprising— modern neural…
In the first part of the talk, I will introduce Multi-agent Generative Adversarial Imitation Learning, a new framework for multi-agent imitation learning for general Markov games, where we build upon a generalized notion of inverse…
We will discuss stochastic processes, Le Jan-Sznitman cascades, that can be associated with certain nonlinear PDE and how explosion of these cascades can be exploited to prove non-uniques for the associated Cauchy problems. In particular,…
The 20th Northwest Probability Seminar, a one-day mini-conference organized by the University of Washington, the Oregon State University, the University of British Columbia, the University of Oregon, and Microsoft Research, was held on October 20,…
