Title: Off the Beaten Path: Domain-Agnostic ML for Controllable Generation and Beyond

Abstract: In many fields of machine learning, the diversity of data domains studied by researchers is significantly more narrow than the diversity of domains in the real world. This has two disadvantages: 1) Existing methods are domain-specific, and fail to serve many impactful domains, including medical and scientific applications, and 2) Failure to examine a broader diversity of data makes it challenging to uncover broader principles underpinning the success of methods across domains. In this talk, I will discuss some of our work on developing machine learning techniques that operate on a wider diversity of data, including a new modeling framework (viewmaker networks) and benchmark (DABS) for self-supervised learning. I will then turn to controllable generation, discussing our work on controllable generation of molecular edits (C5T5), which leverages techniques from both the NLP and drug design communities. I will conclude by discussing future directions and opportunities for domain-agnostic ML in controllable generation and beyond.

Bio: Alex is a fourth-year PhD student in Computer Science at Stanford, advised by Noah Goodman and part of the Stanford NLP Group. His research focuses on better understanding, building, and controlling pretrained models, especially in domain-agnostic and multimodal settings. He is supported by an Open Philanthropy AI Fellowship, and has also spent time at Google Brain and Google Language.