Monday 25 March 14:00-16:00 - Two talks on adversarial robustness and causal representation learning - Zijian Guo (Rutgers) and Bryon Aragam (UChicago)

[Sara Magliacane]

Hi all, 

While we are waiting for the next Amsterdam Causality meeting on April 22, I wanted to invite you all to a related event, an impromptu seminar on causality and robustness with two invited talks by Zijian Guo (Rutgers) and Bryon Aragam (UChicago) next Monday, March 25th from 14:00-16:00 in L3.36 in Lab 42. See details below.

Cheers,
Sara
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When? March 25th 2024 at 14:00-16:00
Where? Lab 42 L3.36

14:00-15:00 Zijian Guo (Rutgers University): Adversarially Robust Learning: Identification, Estimation, and Uncertainty Quantification

Abstract: Empirical risk minimization may lead to poor prediction performance when the target distribution differs from the source populations. This talk discusses leveraging data from multiple sources and constructing more generalizable and transportable prediction models. We introduce an adversarially robust prediction model to optimize a worst-case reward concerning a class of target distributions and show that our introduced model is a weighted average of the source populations' conditional outcome models. We leverage this identification result to robustify arbitrary machine learning algorithms, including, for example, high-dimensional regression, random forests, and neural networks.
In our adversarial learning framework, we propose a novel sampling method to quantify the uncertainty of the adversarial robust prediction model. Moreover, we introduce guided adversarially robust transfer learning (GART) that uses a small amount of target domain data to guide adversarial learning. We show that GART achieves a faster convergence rate than the model fitted with the target data. Our comprehensive simulation studies suggest that GART can substantially outperform existing transfer learning methods, attaining higher robustness and accuracy.

Bio: Zijian Guo is an Associate Professor in Department of Statistics, at Rutgers University. He received my Ph.D. in Statistics at University of Pennsylvania in 2017 and he was fortunate to be advised by Professor T. Tony Cai. Before that, he received his bachelor degree in Mathematics from The Chinese University of Hong Kong.

15:00-16:00 Bryon Aragam (U Chicago): Statistical aspects of nonparametric latent variable models and causal representation learning

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. A crucial step in this pipeline is to identify latent representations from observational data along with their causal structure. In many applications, the causal variables are not directly observed, and must be learned from data, often using flexible, nonparametric models such as deep neural networks. These settings present new statistical and computational challenges that will be the focus of this talk. We will re-visit the statistical foundations of nonparametric latent variable models as a lens into the problem of causal representation learning. We discuss our recent work on developing methods for identifying and learning causal representations from data with rigourous guarantees, and discuss how even basic statistical properties are surprisingly subtle. Along the way, we will explore the connections between causal graphical models, deep generative models, and nonparametric mixture models, and how these connections lead to a useful new theory for causal representation learning.

Bio: Bryon Aragam studies statistical machine learning, nonparametric statistics, and unsupervised learning. His current interests involve (i) Statistical aspects of latent variable models, (ii) Model selection and identification in nonparametric models, and (iii) Theory and computation for deep generative models. In particular, this work focuses on applications of artificial intelligence, including tools such as ChatGPT and DALL-E. His work attempts to understand the statistical foundations of these models and how to improve them from both practical and theoretical perspectives. He is also involved with developing open-source software and solving problems in interpretability, ethics, and fairness in artificial intelligence. 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.

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