Bio: Aditi Raghunathan is an Assistant Professor at Carnegie Mellon University. She is interested in building robust ML systems with guarantees for trustworthy real-world deployment. Previously, she was a postdoctoral researcher at Berkeley AI Research, and received her PhD from Stanford University in 2021. Her research has been recognized by the Schmidt AI2050 Early Career Fellowship, the Arthur Samuel Best Thesis Award at Stanford, a Google PhD fellowship in machine learning, and an Open Philanthropy AI fellowship.

Title: Beyond Adversaries: Robustness to Distribution Shifts in the Wild

Abstract: Machine learning systems often fail catastrophically under the presence of distribution shift—when the test distribution differs in some systematic way from the training distribution. Such shifts can sometimes be captured via an adversarial threat model, but in many cases, there is no convenient threat model that appropriately captures the “real-world” distribution shift. In this talk, we will first discuss how to measure the robustness to such distribution shifts despite the apparent lack of structure. Next, we discuss how to improve robustness to such shifts. The past few years have seen the rise of large models trained on broad data at scale that can be adapted to several downstream tasks (e.g. BERT, GPT, DALL-E). Via theory and experiments, we will see how such models open up new avenues but also require new techniques for improving robustness.