ASSET Seminar: Domain Adaptation Under Causally Structured Distribution Shifts (Zachary Lipton, Carnegie Mellon University)

Presentation Abstract: 

Faced with unlabeled data in deployment that is sampled from a different distribution than that which generated the training data, all bets are off. Moreover, while numerous heuristics have been proposed for this vague setting, it remains unclear when any among them are applicable. One way to render these problems identifiable is to impose some (assumed) causal structure, both over how the variables are related to each other, which factors are potentially manipulable (and, complementarily, which are domain-invariant). Unlike conventional problems in causality, where the goal is to estimate the effect of a manipulation (a change in the policy for prescribing the treatment), here the manipulation has already happened, and our goal is to leverage the causal structure to adapt our predictors appropriately. In this talk, I will discuss some structures under which these problems are identifiable and some of the challenges (and solutions) for applying these ideas in deep learning settings.

Speaker Bio:

Zachary Chase Lipton is an Assistant Professor of Machine Learning and Operations Research at Carnegie Mellon University and a Visiting Scientist at Amazon AI. He directs the Approximately Correct Machine Intelligence (ACMI) lab, whose research focuses including the theoretical and engineering foundations of robust and adaptive machine learning algorithms, applications to both prediction and decision-making problems in clinical medicine, natural language processing, and the impact of machine learning systems on society. A key theme in his current work is to take advantage of causal structure underlying the observed data while dealing with the messy high-dimensional data that typifies deep learning settings. He is the founder of the Approximately Correct blog (approximatelycorrect.com) and a co-author of Dive Into Deep Learning, an interactive open-source book drafted entirely through Jupyter notebooks. He can be found on Twitter (@zacharylipton), GitHub (@zackchase), or his lab’s website.