Bryon Aragam studies statistical machine learning, unsupervised learning (graphical models, representation learning, latent variable models, etc.), nonparametric and high-dimensional statistics, and causal inference. He is also involved with developing open-source software and solving problems in interpretability, ethics, and fairness in artificial intelligence. His work has been published in top statistics and machine learning venues such as the Annals of Statistics, Neural Information Processing Systems, the International Conference on Machine Learning, and the Journal of Machine Learning Research.
Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. He completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow. Bryon has also served as a data science consultant for technology and marketing firms, where he has worked on problems in survey design and methodology, ranking, customer retention, and logistics.
Talk: A modern approach to nonparametric latent variable models and representation learning: Identifiability, consistency, and a nonstandard minimax rate
Abstract: One of the key paradigm shifts in statistical machine learning over the past decade has been the transition from handcrafted features to automated, data-driven representation learning, typically via deep neural networks. As these methods are being used in high stakes settings such as medicine, health care, law, and finance where accountability and transparency are not just desirable but often legally required, it has become necessary to place representation learning on a rigourous scientific footing. In this talk we will re-visit the statistical foundations of nonparametric latent variable models, and discuss how even basic statistical properties such as identifiability and consistency are surprisingly subtle. We will also discuss new results characterizing the optimal sample complexity for learning simple nonparametric mixtures, which turns out to have a nonstandard super-polynomial bound. Time permitting, we will end with applications to deep generative models that are widely used in practice.